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BOOLEAN
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BOOLEAN
Natural Language Understanding (NLU): We implemented an NLU unit utilizing handcrafted rules, Regular Expressions (RegEx) and Elasticsearch (ES) API. The NLU module contains following functionalities: Dialogue Manager consists of the Dialogue State Tracker (DST), that maintains a representation of the current dialog state, and of the Policy Learner (PL) that defines the next system action. In our model, the system's next action is defined by the state of the previously obtained information stored in the Information Dictionary. For instance, if the system recognizes that the student works on the final examination, it also understands (defined by the logic in the predefined rules) that there is no need to ask for sub-topic because the final examination always corresponds to a chapter level (due to the design of OMB+ platform). If the system identifies that the user has difficulties in solving a quiz, it has to ask for the corresponding topic and sub-topic if not yet provided by a user (because the quiz always refers to a section level). To determine all of the potential dialogue flows, we implemented Mutually Exclusive Rules (MER), which indicate that two events $e_{1}$ and $e_{2}$ are mutually exclusive or disjoint if they cannot both occur at the same time (thus, the intersection of these events is empty: $P(A \cap B) = 0$). Additionally, we defined transition and mapping rules. The formal explanation of rules can be found in Section SECREF12 of the Appendix. Following the rules, we generated 56 state transitions, which define next system actions. Being on a new dialogue state, the system compares the extracted (i.e., updated) information in the ID with the valid dialogue states (see Section SECREF12 of the Appendix for the explanation of the validness) and picks the mapped action as the next system's action.
2001.02284
Introduction </s> Rob otic ĠProcess ĠAutom ation Ġ( R PA ) Ġis Ġa Ġtype Ġof Ġsoftware Ġbots Ġthat Ġsim ulates Ġhand - operated Ġhuman Ġactivities Ġlike Ġentering Ġdata Ġinto Ġa Ġsystem , Ġregistering Ġinto Ġaccounts , Ġand Ġaccompl ishing Ġstraightforward Ġbut Ġrepetitive Ġwork flows ĠB IB REF 0 . ĠHowever , Ġone Ġof Ġthe Ġdrawbacks Ġof ĠR PA - bots Ġis Ġtheir Ġsusceptibility Ġto Ġchanges Ġin Ġdefined Ġscenarios : Ġbeing Ġdesigned Ġfor Ġa Ġparticular Ġtask , Ġthe ĠR PA - bot Ġis Ġusually Ġnot Ġadapt able Ġto Ġother Ġdomains Ġor Ġeven Ġlight Ġmodifications Ġin Ġa Ġworkflow ĠB IB REF 0 . ĠThis Ġinability Ġto Ġread just Ġto Ġshifting Ġconditions Ġgave Ġrise Ġto ĠIntelligent ĠProcess ĠAutom ation Ġ( IP A ) Ġsystems . ĠIPA - bots Ġcombine ĠR PA Ġwith ĠArtificial ĠIntelligence Ġ( AI ) Ġand Ġthus Ġare Ġable Ġto Ġexecute Ġmore Ġcognitive ly Ġdemanding Ġtasks Ġthat Ġrequire Ġi . a . Ġreasoning Ġand Ġlanguage Ġunderstanding . ĠHence , ĠIPA - bots Ġadvanced Ġbeyond Ġautom ating Ġshallow ĠâĢ ľ click Ġtasks âĢ Ŀ Ġand Ġcan Ġperform Ġjobs Ġmore Ġintellig ently ĠâĢĵ Ġby Ġmeans Ġof Ġmachine Ġlearning Ġalgorithms . ĠSuch ĠIPA - system s Ġundertake Ġtime - consuming Ġand Ġroutine Ġtasks , Ġand Ġthus Ġenable Ġsmart Ġwork flows Ġand Ġfree Ġup Ġskilled Ġworkers Ġto Ġaccomplish Ġhigher - value Ġactivities . </s> One Ġof Ġthe Ġpotential Ġapplications Ġof ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġwithin Ġthe ĠIPA Ġdomain Ġare Ġconvers ational Ġinterfaces Ġthat Ġenable Ġhuman - to - machine Ġinteraction . ĠThe Ġmain Ġbenefit Ġof Ġconvers ational Ġsystems Ġis Ġtheir Ġability Ġto Ġgive Ġattention Ġto Ġseveral Ġusers Ġsimultaneously Ġwhile Ġsupporting Ġnatural Ġcommunication . ĠA Ġconventional Ġdialogue Ġsystem Ġcomprises Ġmultiple Ġstages Ġand Ġinvolves Ġdifferent Ġtypes Ġof ĠN LP Ġsubt asks , Ġstarting Ġwith ĠNatural ĠLanguage ĠUnderstanding Ġ( NL U ) Ġ( e . g ., Ġintent Ġclassification , Ġnamed Ġentity Ġextraction ) Ġand Ġgoing Ġtowards Ġdialogue Ġmanagement Ġ( i . e ., Ġdetermining Ġthe Ġnext Ġpossible Ġbot Ġaction , Ġconsidering Ġthe Ġdialogue Ġhistory ) Ġand Ġresponse Ġgeneration Ġ( e . g ., Ġconverting Ġthe Ġsemantic Ġrepresentation Ġof Ġthe Ġnext Ġsystem Ġaction Ġinto Ġa Ġnatural Ġlanguage Ġutter ance ). ĠA Ġtypical Ġdialogue Ġsystem Ġfor ĠIPA Ġpurposes Ġundert akes Ġshallow Ġcustomer Ġsupport Ġrequests Ġ( e . g ., Ġanswering Ġof ĠFAQ s ), Ġallowing Ġhuman Ġworkers Ġto Ġfocus Ġon Ġmore Ġsophisticated Ġinquiries . </s> Recent Ġresearch Ġin Ġthe Ġdialogue Ġgeneration Ġdomain Ġis Ġconducted Ġby Ġemploying ĠAI - techn iques Ġlike Ġmachine Ġand Ġdeep Ġlearning ĠB IB REF 1 , ĠB IB REF 2 . ĠHowever , Ġconventional Ġsupervised Ġmethods Ġhave Ġlimitations Ġwhen Ġapplied Ġto Ġreal - world Ġdata Ġand Ġindustrial Ġtasks . ĠThe Ġprimary Ġchallenge Ġhere Ġrefers Ġto Ġa Ġtraining Ġphase Ġsince Ġa Ġrobust Ġmodel Ġrequires Ġan Ġextensive Ġamount Ġof Ġstructured Ġand Ġlabeled Ġdata , Ġthat Ġis Ġoften Ġnot Ġavailable Ġfor Ġdomain - specific Ġproblems . ĠEspecially Ġif Ġit Ġconcerns Ġdialogue Ġdata , Ġwhich Ġhas Ġto Ġbe Ġappropriately Ġstructured Ġas Ġwell Ġas Ġlabeled Ġand Ġannot ated Ġwith Ġadditional Ġinformation . ĠTherefore , Ġdespite Ġthe Ġpopularity Ġof Ġdeep Ġlearning Ġend - to - end Ġmodels , Ġone Ġstill Ġneeds Ġto Ġrely Ġon Ġconventional Ġpipelines Ġin Ġpractical Ġdialogue Ġengineering , Ġespecially Ġwhile Ġsetting Ġa Ġnew Ġdomain . ĠHowever , Ġwith Ġfew Ġstructured Ġdata Ġavailable , Ġtransfer Ġlearning Ġmethods Ġcan Ġbe Ġused . ĠSuch Ġalgorithms Ġenable Ġtraining Ġof Ġthe Ġsystems Ġwith Ġless Ġor Ġeven Ġa Ġminimal Ġamount Ġof Ġdata , Ġand Ġare Ġable Ġto Ġtransfer Ġthe Ġknowledge Ġobtained Ġduring Ġthe Ġtraining Ġon Ġexisting Ġdata Ġto Ġthe Ġunseen Ġdomain . </s> Introduction Ġ: :: ĠOut line Ġand ĠContributions </s> This Ġpaper Ġaddresses Ġthe Ġchallenge Ġof Ġimplementing Ġa Ġdialogue Ġsystem Ġfor ĠIPA Ġpurposes Ġwithin Ġthe Ġpractical Ġe - learning Ġdomain Ġwith Ġthe Ġinitial Ġabsence Ġof Ġtraining Ġdata . ĠOur Ġcontributions Ġwithin Ġthis Ġwork Ġare Ġas Ġfollows : </s> We Ġimplemented Ġa Ġrobust Ġdialogue Ġsystem Ġfor ĠIPA Ġpurposes Ġwithin Ġthe Ġpractical Ġe - learning Ġdomain Ġand Ġwithin Ġthe Ġconditions Ġof Ġmissing Ġtraining Ġ( dial ogue ) Ġdata Ġ( see ĠSection ĠSEC REF 3 ĠâĢĵ ĠSection ĠSEC REF 4 ). ĠThe Ġsystem Ġis Ġcurrently Ġdeployed Ġat Ġthe Ġe - learning Ġplatform . </s> The Ġsystem Ġhas Ġtwo Ġpurposes : </s> First , Ġit Ġreduces Ġrepetitive Ġand Ġtime - consuming Ġactivities Ġand , Ġtherefore , Ġallows Ġworkers Ġof Ġthe Ġe - learning Ġplatform Ġto Ġfocus Ġsolely Ġon Ġcomplex Ġquestions ; </s> Second , Ġby Ġinteracting Ġwith Ġusers , Ġit Ġaug ments Ġthe Ġresources Ġwith Ġstructured Ġand Ġto Ġsome Ġextent Ġlabeled Ġtraining Ġdata Ġfor Ġfurther Ġpossible Ġimplementation Ġof Ġlearn able Ġdialogue Ġcomponents Ġ( see ĠSection ĠSEC REF 5 ); </s> We Ġshowcased Ġthat Ġeven Ġa Ġsmall Ġamount Ġof Ġstructured Ġdialog ues Ġcould Ġbe Ġsuccessfully Ġused Ġfor Ġre - training Ġof Ġdialogue Ġunits Ġby Ġmeans Ġof ĠTransfer ĠLearning Ġtechniques Ġ( see ĠSection ĠSEC REF 6 ). </s> Target ĠDomain Ġ& ĠTask ĠDefinition </s> OM B + Ġis Ġa ĠGerman Ġe - learning Ġplatform Ġthat Ġassists Ġstudents Ġwho Ġare Ġpreparing Ġfor Ġan Ġengineering Ġor Ġcomputer Ġscience Ġstudy Ġat Ġa Ġuniversity . ĠThe Ġcentral Ġpurpose Ġof Ġthe Ġcourse Ġis Ġto Ġsupport Ġstudents Ġin Ġrev iving Ġtheir Ġmathematical Ġskills Ġso Ġthat Ġthey Ġcan Ġfollow Ġthe Ġupcoming Ġuniversity Ġcourses . ĠThe Ġplatform Ġis Ġthem atically Ġsegment ed Ġinto Ġ13 Ġsections Ġand Ġincludes Ġfree Ġmathematical Ġclasses Ġwith Ġtheoretical Ġand Ġpractical Ġcontent . ĠBesides Ġthat , ĠO MB + Ġprovides Ġa Ġpossibility Ġto Ġget Ġassistance Ġfrom Ġa Ġhuman Ġtutor Ġvia Ġa Ġchat Ġinterface . ĠUsually , Ġthe Ġstudents Ġand Ġtut ors Ġinteract Ġin Ġwritten Ġform , Ġand Ġthe Ġlanguage Ġof Ġcommunication Ġis ĠGerman . ĠThe Ġcurrent Ġproblem Ġof Ġthe ĠO MB + Ġplatform Ġis Ġthat Ġthe Ġnumber Ġof Ġstudents Ġgrows Ġevery Ġyear , Ġbut Ġto Ġhire Ġmore Ġqualified Ġhuman Ġtut ors Ġis Ġchallenging Ġand Ġexpensive . ĠThis Ġresults Ġin Ġa Ġmore Ġextended Ġwaiting Ġperiod Ġfor Ġstudents Ġuntil Ġtheir Ġproblems Ġcan Ġbe Ġconsidered . </s> In Ġgeneral , Ġstudent Ġquestions Ġcan Ġbe Ġgrouped Ġinto Ġthree Ġmain Ġcategories : Ġorganizational Ġquestions Ġ( e . g ., Ġcourse Ġcertificate ), Ġcontextual Ġquestions Ġ( e . g ., Ġcontent , Ġtheorem ) Ġand Ġmathematical Ġquestions Ġ( e . g ., Ġexercises , Ġsolutions ). ĠTo Ġassist Ġa Ġstudent Ġwith Ġa Ġmathematical Ġquestion , Ġa Ġtutor Ġhas Ġto Ġknow Ġthe Ġfollowing Ġregular Ġinformation : ĠWhat Ġkind Ġof Ġtopic Ġ( or Ġsub - topic ) Ġa Ġstudent Ġhas Ġa Ġproblem Ġwith . ĠAt Ġwhich Ġexamination Ġmode Ġ( i . e ., Ġquiz , Ġchapter Ġlevel Ġtraining Ġor Ġexercise , Ġsection Ġlevel Ġtraining Ġor Ġexercise , Ġor Ġfinal Ġexamination ) Ġthe Ġstudent Ġis Ġworking Ġright Ġnow . ĠAnd Ġfinally , Ġthe Ġexact Ġquestion Ġnumber Ġand Ġexact Ġproblem Ġformulation . ĠThis Ġmeans Ġthat Ġa Ġtutor Ġhas Ġto Ġrequest Ġthe Ġsame Ġinformation Ġevery Ġtime Ġa Ġnew Ġdialogue Ġopens , Ġwhich Ġis Ġvery Ġtime Ġconsuming Ġand Ġcould Ġbe Ġsuccessfully Ġsolved Ġby Ġmeans Ġof Ġan ĠIPA Ġdialogue Ġbot . </s> Model </s> The Ġmain Ġobjective Ġof Ġthe Ġproposed Ġsystem Ġis Ġto Ġinteract Ġwith Ġstudents Ġat Ġthe Ġbeginning Ġof Ġevery Ġconversation Ġand Ġgather Ġinformation Ġon Ġthe Ġtopic Ġ( and Ġsub - topic ), Ġexamination Ġmode Ġand Ġlevel , Ġquestion Ġnumber Ġand Ġexact Ġproblem Ġformulation . ĠTherefore , Ġthe Ġsystem Ġsaves Ġtime Ġfor Ġtut ors Ġand Ġallows Ġthem Ġto Ġhandle Ġsolely Ġcomplex Ġmathematical Ġquestions . ĠBesides Ġthat , Ġthe Ġsystem Ġis Ġimplemented Ġin Ġa Ġway Ġsuch Ġthat Ġit Ġaccum ulates Ġlabeled Ġdialog ues Ġin Ġthe Ġbackground Ġand Ġstores Ġthem Ġin Ġa Ġstructured Ġform . </s> Model Ġ: :: ĠDialogue ĠMod ules </s> Figure ĠFIG REF 82 Ġ( see Ġthe ĠAppendix ) Ġdisplays Ġthe Ġentire Ġdialogue Ġflow . ĠIn Ġa Ġnutshell , Ġthe Ġsystem Ġreceives Ġa Ġuser Ġinput , Ġanaly zes Ġit Ġand Ġextracts Ġinformation , Ġif Ġprovided . ĠIf Ġsome Ġof Ġthe Ġrequired Ġinformation Ġis Ġmissing , Ġthe Ġsystem Ġasks Ġthe Ġstudent Ġto Ġprovide Ġit . ĠWhen Ġall Ġthe Ġinformation Ġis Ġcollected , Ġit Ġwill Ġbe Ġautomatically Ġvalidated Ġand Ġsubsequently Ġforwarded Ġto Ġa Ġhuman Ġtutor , Ġwho Ġthen Ġcan Ġdirectly Ġproceed Ġwith Ġthe Ġassistance . ĠIn Ġthe Ġfollowing Ġwe Ġwill Ġdescribe Ġthe Ġcentral Ġcomponents Ġof Ġthe Ġsystem . </s> OM B + ĠDesign : ĠFigure ĠFIG REF 52 Ġ( see ĠSection ĠSEC REF 11 Ġof Ġthe ĠAppendix ) Ġillustrates Ġthe Ġinternal Ġstructure Ġand Ġdesign Ġof Ġthe ĠO MB + Ġplatform . ĠIt Ġhas Ġtopics Ġand Ġsub - top ics , Ġas Ġwell Ġas Ġfour Ġexamination Ġmodes . ĠEach Ġtopic Ġ( Figure ĠFIG REF 52 , Ġtag Ġ1 ) Ġcorresponds Ġto Ġa Ġchapter Ġlevel Ġand Ġalways Ġhas Ġsub - top ics Ġ( Figure ĠFIG REF 52 , Ġtag Ġ2 ), Ġwhich Ġcorrespond Ġto Ġa Ġsection Ġlevel . ĠExamination Ġmodes Ġtraining Ġand Ġexercise Ġare Ġambiguous , Ġbecause Ġthey Ġcorrespond Ġto Ġeither Ġa Ġchapter Ġ( Figure ĠFIG REF 52 , Ġtag Ġ3 ) Ġor Ġa Ġsection Ġ( Figure ĠFIG REF 52 , Ġtag Ġ5 ) Ġlevel , Ġand Ġit Ġis Ġimportant Ġto Ġdifferentiate Ġbetween Ġthem , Ġsince Ġthey Ġcontain Ġdifferent Ġtypes Ġof Ġcontent . ĠThe Ġmode Ġfinal Ġexamination Ġ( Figure ĠFIG REF 52 , Ġtag Ġ4 ) Ġalways Ġcorresponds Ġto Ġa Ġchapter Ġlevel , Ġwhereas Ġquiz Ġ( Figure ĠFIG REF 52 , Ġtag Ġ5 ) Ġcan Ġbelong Ġonly Ġto Ġa Ġsection Ġlevel . ĠAccording Ġto Ġthe Ġdesign Ġof Ġthe ĠO MB + Ġplatform , Ġthere Ġare Ġseveral Ġways Ġof Ġhow Ġa Ġpossible Ġdialogue Ġflow Ġcan Ġproceed . </s> Pre processing : ĠIn Ġa Ġnatural Ġlanguage Ġdialogue , Ġa Ġuser Ġmay Ġrespond Ġin Ġmany Ġdifferent Ġways , Ġthus , Ġthe Ġextraction Ġof Ġany Ġdata Ġfrom Ġuser - generated Ġtext Ġis Ġa Ġchallenging Ġtask Ġdue Ġto Ġa Ġnumber Ġof Ġmiss pell ings Ġor Ġconf usable Ġspell ings Ġ( e . g ., ĠExercise Ġ1 . a , ĠExercise Ġ1 Ġ( a )). ĠTherefore , Ġto Ġenable Ġa Ġreliable Ġextraction Ġof Ġentities , Ġwe Ġpre process ed Ġand Ġnormalized Ġ( e . g ., Ġmiss pell ings , Ġsyn onyms ) Ġevery Ġuser Ġinput Ġbefore Ġit Ġwas Ġsent Ġto Ġthe ĠNatural ĠLanguage ĠUnderstanding Ġ( NL U ) Ġmodule . ĠThe Ġpre processing Ġincludes Ġfollowing Ġsteps : </s> lower c asing Ġand Ġstemming Ġof Ġall Ġwords Ġin Ġthe Ġinput ; </s> rem oval Ġof ĠGerman Ġstop Ġwords Ġand Ġpunct uation ; </s> all Ġmentions Ġof Ġ$ x $ Ġin Ġmathematical Ġformulas Ġwere Ġremoved Ġto Ġavoid Ġconfusion Ġwith Ġr oman Ġnumber Ġ10 Ġ( âĢ ľ $ X $ âĢ Ŀ ); </s> in Ġa Ġcombination Ġof Ġthe Ġtype : Ġword ĠâĢ ľ Chapter / Ex ercise âĢ Ŀ Ġ+ Ġdigit Ġwritten Ġas Ġa Ġword Ġ( i . e ĠâĢ ľ first âĢ Ŀ , ĠâĢ ľ second âĢ Ŀ ), Ġword Ġwas Ġreplaced Ġwith Ġa Ġdigit Ġ( âĢ ľ in Ġfirst ĠChapter âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ in ĠChapter Ġ1 âĢ Ŀ ), Ġr oman Ġnumbers Ġwere Ġreplaced Ġwith Ġdigits Ġas Ġwell Ġ( âĢ ľ Chapter ĠIV âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ Chapter Ġ4 âĢ Ŀ ). </s> det ected Ġambig u ities Ġwere Ġnormalized Ġ( e . g ., ĠâĢ ľ Train ings au fg abe âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ Training âĢ Ŀ ); </s> recogn ized Ġmiss pell ings Ġresp . Ġty pos Ġwere Ġcorrected Ġ( e . g ., ĠâĢ ľ D ife ern z ial re chn ung âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ Different ial re chn ung ') ' </s> per mal inks Ġwere Ġparsed Ġand Ġanalyzed . ĠFrom Ġeach Ġper malink Ġit Ġis Ġpossible Ġto Ġextract Ġtopic , Ġexamination Ġmode Ġand Ġquestion Ġnumber ; </s> Natural ĠLanguage ĠUnderstanding Ġ( NL U ): ĠWe Ġimplemented Ġan ĠNL U Ġunit Ġutilizing Ġhand crafted Ġrules , ĠRegular ĠExpress ions Ġ( Reg Ex ) Ġand ĠElastic search Ġ( ES ) ĠAPI . ĠThe ĠNL U Ġmodule Ġcontains Ġfollowing Ġfunctional ities : </s> Int ent Ġclassification : ĠAs Ġwe Ġmentioned Ġabove , Ġstudent Ġquestions Ġcan Ġbe Ġgrouped Ġinto Ġthree Ġmain Ġcategories : ĠOrgan izational Ġquestions , Ġcontextual Ġquestions Ġand Ġmathematical Ġquestions . ĠTo Ġclassify Ġthe Ġinput Ġmessage Ġby Ġits Ġcategory Ġor Ġso - called Ġintent , Ġwe Ġutilized Ġkey - word Ġinformation Ġpred efined Ġby Ġhand crafted Ġrules . ĠWe Ġassumed Ġthat Ġparticular Ġwords Ġare Ġexplicit Ġand Ġassociated Ġwith Ġa Ġcorresponding Ġintent . ĠIf Ġno Ġintent Ġcould Ġbe Ġclassified , Ġthen Ġit Ġis Ġassumed Ġthat Ġthe ĠNL U Ġunit Ġwas Ġnot Ġcapable Ġof Ġunderstanding Ġand Ġthe Ġintent Ġis Ġinterpreted Ġas Ġunknown . ĠIn Ġthis Ġcase , Ġthe Ġsystem Ġrequests Ġthe Ġuser Ġto Ġprovide Ġan Ġintent Ġmanually Ġ( by Ġpicking Ġone Ġfrom Ġthe Ġmentioned Ġthree Ġoptions ). ĠThe Ġquestions Ġfrom Ġorganizational Ġand Ġtheoretical Ġcategories Ġare Ġdirectly Ġdelivered Ġto Ġa Ġhuman Ġtutor , Ġwhile Ġmathematical Ġquestions Ġare Ġprocessed Ġby Ġthe Ġautomated Ġsystem Ġfor Ġfurther Ġanalysis . </s> Entity ĠExt raction : ĠNext , Ġthe Ġsystem Ġattempts Ġto Ġretrieve Ġthe Ġentities Ġfrom Ġa Ġuser Ġmessage Ġon Ġthe Ġtopic Ġ( and Ġsub - topic ), Ġexamination Ġmode Ġand Ġlevel , Ġand Ġquestion Ġnumber . ĠThis Ġpart Ġis Ġimplemented Ġusing ĠElastic search Ġ( ES ) Ġand ĠReg Ex . ĠTo Ġenable Ġthe Ġuse Ġof ĠES , Ġwe Ġindexed Ġthe ĠO MB + Ġsite Ġto Ġan Ġinternal Ġdatabase . ĠBesides Ġindex ing Ġthe Ġtopics Ġand Ġtitles , Ġwe Ġalso Ġprovided Ġinformation Ġon Ġpossible Ġsyn onyms Ġor Ġwriting Ġstyles . ĠWe Ġadditionally Ġfiled ĠO MB + Ġper mal inks , Ġwhich Ġdirect Ġto Ġthe Ġsite Ġpages . ĠTo Ġquery Ġthe Ġresulting Ġdatabase , Ġwe Ġutilized Ġthe Ġinternal ĠElastic search Ġmulti Ġmatch Ġfunction Ġand Ġset Ġthe Ġminimum Ġshould Ġmatch Ġparameter Ġto Ġ$ 20 \ % $. ĠThis Ġparameter Ġdefines Ġthe Ġnumber Ġof Ġterms Ġthat Ġmust Ġmatch Ġfor Ġa Ġdocument Ġto Ġbe Ġconsidered Ġrelevant . ĠBesides Ġthat , Ġwe Ġadopted Ġfuzz iness Ġwith Ġthe Ġmaximum Ġedit Ġdistance Ġset Ġto Ġ2 Ġcharacters . ĠThe Ġfuzzy Ġquery Ġuses Ġsimilarity Ġbased Ġon ĠLe ven sh tein Ġedit Ġdistance ĠB IB REF 3 . ĠFinally , Ġthe Ġsystem Ġgenerates Ġa Ġranked Ġlist Ġof Ġpossible Ġmatching Ġentries Ġfound Ġin Ġthe Ġdatabase Ġwithin Ġthe Ġpred efined Ġrelevance Ġthreshold Ġ( we Ġset Ġit Ġto Ġ$\ the ta Ġ$ =$ 1 . 5 $ ). ĠWe Ġpick Ġthe Ġmost Ġprobable Ġentry Ġas Ġthe Ġcorrect Ġone Ġand Ġextract Ġthe Ġcorresponding Ġentity Ġfrom Ġthe Ġuser Ġinput . </s> To Ġsummarize , Ġthe ĠNL U Ġmodule Ġreceives Ġthe Ġuser Ġinput Ġas Ġa Ġpre process ed Ġtext Ġand Ġchecks Ġit Ġacross Ġall Ġpred efined ĠReg Ex Ġstatements Ġand Ġfor Ġa Ġmatch Ġin Ġthe ĠElastic search Ġdatabase . ĠEvery Ġtime Ġthe Ġentity Ġis Ġextracted , Ġit Ġis Ġentered Ġin Ġthe ĠInformation ĠDictionary Ġ( ID ). ĠThe ĠID Ġhas Ġthe Ġfollowing Ġsix Ġslots Ġto Ġbe Ġfilled Ġin : Ġtopic , Ġsub - topic , Ġexamination Ġlevel , Ġexamination Ġmode , Ġquestion Ġnumber , Ġand Ġexact Ġproblem Ġformulation . </s> Dialogue ĠManager Ġconsists Ġof Ġthe ĠDialogue ĠState ĠTracker Ġ( D ST ), Ġthat Ġmaintains Ġa Ġrepresentation Ġof Ġthe Ġcurrent Ġdialog Ġstate , Ġand Ġof Ġthe ĠPolicy ĠLear ner Ġ( PL ) Ġthat Ġdefines Ġthe Ġnext Ġsystem Ġaction . ĠIn Ġour Ġmodel , Ġthe Ġsystem 's Ġnext Ġaction Ġis Ġdefined Ġby Ġthe Ġstate Ġof Ġthe Ġpreviously Ġobtained Ġinformation Ġstored Ġin Ġthe ĠInformation ĠDictionary . ĠFor Ġinstance , Ġif Ġthe Ġsystem Ġrecognizes Ġthat Ġthe Ġstudent Ġworks Ġon Ġthe Ġfinal Ġexamination , Ġit Ġalso Ġunderstands Ġ( defined Ġby Ġthe Ġlogic Ġin Ġthe Ġpred efined Ġrules ) Ġthat Ġthere Ġis Ġno Ġneed Ġto Ġask Ġfor Ġsub - topic Ġbecause Ġthe Ġfinal Ġexamination Ġalways Ġcorresponds Ġto Ġa Ġchapter Ġlevel Ġ( due Ġto Ġthe Ġdesign Ġof ĠO MB + Ġplatform ). ĠIf Ġthe Ġsystem Ġidentifies Ġthat Ġthe Ġuser Ġhas Ġdifficulties Ġin Ġsolving Ġa Ġquiz , Ġit Ġhas Ġto Ġask Ġfor Ġthe Ġcorresponding Ġtopic Ġand Ġsub - topic Ġif Ġnot Ġyet Ġprovided Ġby Ġa Ġuser Ġ( because Ġthe Ġquiz Ġalways Ġrefers Ġto Ġa Ġsection Ġlevel ). ĠTo Ġdetermine Ġall Ġof Ġthe Ġpotential Ġdialogue Ġflows , Ġwe Ġimplemented ĠMut ually ĠExclusive ĠRules Ġ( MER ), Ġwhich Ġindicate Ġthat Ġtwo Ġevents Ġ$ e _{ 1 } $ Ġand Ġ$ e _{ 2 } $ Ġare Ġmutually Ġexclusive Ġor Ġdis j oint Ġif Ġthey Ġcannot Ġboth Ġoccur Ġat Ġthe Ġsame Ġtime Ġ( thus , Ġthe Ġintersection Ġof Ġthese Ġevents Ġis Ġempty : Ġ$ P ( A Ġ\ cap ĠB ) Ġ= Ġ0 $ ). ĠAdditionally , Ġwe Ġdefined Ġtransition Ġand Ġmapping Ġrules . ĠThe Ġformal Ġexplanation Ġof Ġrules Ġcan Ġbe Ġfound Ġin ĠSection ĠSEC REF 12 Ġof Ġthe ĠAppendix . ĠFollowing Ġthe Ġrules , Ġwe Ġgenerated Ġ56 Ġstate Ġtransitions , Ġwhich Ġdefine Ġnext Ġsystem Ġactions . ĠBeing Ġon Ġa Ġnew Ġdialogue Ġstate , Ġthe Ġsystem Ġcompares Ġthe Ġextracted Ġ( i . e ., Ġupdated ) Ġinformation Ġin Ġthe ĠID Ġwith Ġthe Ġvalid Ġdialogue Ġstates Ġ( see ĠSection ĠSEC REF 12 Ġof Ġthe ĠAppendix Ġfor Ġthe Ġexplanation Ġof Ġthe Ġvalid ness ) Ġand Ġpicks Ġthe Ġmapped Ġaction Ġas Ġthe Ġnext Ġsystem 's Ġaction . </s> The Ġabove mentioned Ġrules Ġare Ġintended Ġto Ġsupport Ġthe Ġcurrent Ġdesign Ġof Ġthe ĠO MB + Ġlearning Ġplatform . ĠHowever , Ġadditional ĠMER s Ġcould Ġbe Ġadded Ġto Ġgenerate Ġnew Ġtransitions . ĠEx empl ifying Ġthis , Ġwe Ġconducted Ġexperiments Ġwith Ġthe Ġformer Ġdesign Ġof Ġthe Ġplatform , Ġwhere Ġall Ġthe Ġtopics , Ġexcept Ġfor Ġthe Ġfirst Ġone , Ġhad Ġonly Ġsub - top ics , Ġwhereas Ġthe Ġfirst Ġtopic Ġhad Ġboth Ġsub - top ics Ġand Ġsub - sub - top ics . ĠWe Ġcould Ġeffortlessly Ġgenerate Ġthe Ġmissing Ġtransitions Ġwith Ġour Ġapproach . ĠThe Ġnumber Ġof Ġpossible Ġtransitions , Ġin Ġthis Ġcase , Ġincreased Ġfrom Ġ56 Ġto Ġ117 . </s> Meta ĠPolicy : ĠSince Ġthe Ġsystem Ġis Ġintended Ġto Ġoperate Ġin Ġa Ġreal - world Ġscenario , Ġwe Ġhad Ġto Ġimplement Ġadditional Ġpolicies Ġthat Ġcontrol Ġthe Ġdialogue Ġflow Ġand Ġvalidate Ġthe Ġsystem 's Ġaccuracy . ĠBelow Ġwe Ġdescribe Ġthese Ġpolicies : </s> Com ple teness Ġof Ġthe ĠInformation ĠDictionary : ĠIn Ġthis Ġstep , Ġthe Ġsystem Ġvalid ates Ġthe Ġcomple teness Ġof Ġthe ĠID , Ġwhich Ġis Ġdefined Ġby Ġthe Ġnumber Ġof Ġobligatory Ġslots Ġfilled Ġin Ġthe Ġinformation Ġdictionary . ĠThere Ġare Ġ6 Ġdistinct Ġcases Ġwhen Ġthe ĠID Ġis Ġconsidered Ġto Ġbe Ġcomplete Ġ( see ĠSection ĠSEC REF 13 Ġof Ġthe ĠAppendix ). ĠFor Ġinstance , Ġif Ġa Ġuser Ġworks Ġon Ġa Ġfinal Ġexamination , Ġthe Ġsystem Ġdoes Ġnot Ġhas Ġto Ġrequest Ġa Ġsub - topic Ġor Ġexamination Ġlevel . ĠThus , Ġthe ĠID Ġhas Ġto Ġbe Ġfilled Ġonly Ġwith Ġdata Ġfor Ġa Ġtopic , Ġexamination Ġmode , Ġand Ġquestion Ġnumber , Ġwhereas , Ġif Ġthe Ġuser Ġworks Ġon Ġa Ġquiz , Ġthe Ġsystem Ġhas Ġto Ġgather Ġinformation Ġabout Ġthe Ġtopic , Ġsub - topic , Ġexamination Ġmode , Ġand Ġthe Ġquestion Ġnumber . ĠOnce Ġthe ĠID Ġis Ġcomplete , Ġit Ġis Ġprovided Ġto Ġthe Ġverification Ġstep . ĠOtherwise , Ġthe Ġsystem Ġproceeds Ġaccording Ġto Ġthe Ġnext Ġaction . ĠThe Ġsystem Ġextracts Ġthe Ġinformation Ġin Ġeach Ġdialogue Ġstep , Ġand Ġthus Ġif Ġthe Ġuser Ġprovides Ġupdated Ġinformation Ġon Ġany Ġsubject Ġlater Ġin Ġthe Ġdialogue , Ġthe Ġcorresponding Ġslot Ġwill Ġbe Ġupdated Ġin Ġthe ĠID . </s> Ver ification ĠStep : ĠOnce Ġthe Ġsystem Ġhas Ġobtained Ġall Ġthe Ġnecessary Ġinformation Ġ( i . e ., ĠID Ġis Ġcomplete ), Ġit Ġproceeds Ġto Ġthe Ġfinal Ġverification . ĠIn Ġthat Ġstep , Ġthe Ġcollected Ġdata Ġis Ġshown Ġto Ġthe Ġstudent Ġin Ġthe Ġsession . ĠThe Ġstudent Ġis Ġasked Ġto Ġverify Ġthe Ġcorrectness Ġof Ġthe Ġcollected Ġdata Ġand Ġif Ġsome Ġentries Ġare Ġwrong , Ġto Ġcorrect Ġthem . ĠThe ĠID Ġis , Ġwhere Ġnecessary , Ġupdated Ġwith Ġthe Ġuser - provided Ġdata . ĠThis Ġprocedure Ġrepeats Ġuntil Ġthe Ġuser Ġconfirms Ġthe Ġcorrectness Ġof Ġthe Ġassembled Ġdata . </s> Fall back ĠPolicy : ĠIn Ġsome Ġcases , Ġthe Ġsystem Ġfails Ġto Ġderive Ġinformation Ġfrom Ġa Ġstudent Ġquery , Ġeven Ġif Ġthe Ġstudent Ġprovides Ġit . ĠIt Ġis Ġdue Ġto Ġthe ĠElastic search Ġfunctionality Ġand Ġpreviously Ġunseen ĠReg Ex Ġpatterns . ĠIn Ġthese Ġcases , Ġthe Ġsystem Ġre - asks Ġa Ġuser Ġand Ġattempts Ġto Ġretrieve Ġinformation Ġfrom Ġa Ġfollow - up Ġquery . ĠThe Ġmaximum Ġnumber Ġof Ġre - ask Ġattempts Ġis Ġset Ġto Ġthree Ġtimes Ġ($ r = 3 $ ). ĠIf Ġthe Ġsystem Ġis Ġunable Ġto Ġextract Ġinformation Ġafter Ġthree Ġtimes , Ġthe Ġuser Ġinput Ġis Ġconsidered Ġas Ġthe Ġground Ġtruth Ġand Ġsaved Ġto Ġthe Ġappropriate Ġslot Ġin ĠID . ĠAn Ġexception Ġto Ġthis Ġrule Ġapplies Ġwhere Ġthe Ġuser Ġhas Ġto Ġspecify Ġthe Ġintent Ġmanually . ĠIn Ġthis Ġcase , Ġafter Ġthree Ġun classified Ġattempts , Ġa Ġsession Ġis Ġdirectly Ġhanded Ġover Ġto Ġa Ġhuman Ġtutor . </s> Human ĠRequest : ĠIn Ġeach Ġdialogue Ġstate , Ġa Ġuser Ġcan Ġswitch Ġto Ġa Ġhuman Ġtutor . ĠFor Ġthis , Ġa Ġuser Ġcan Ġenter Ġthe Ġhuman Ġkey - word . ĠHence , Ġevery Ġuser Ġmessage Ġis Ġadditionally Ġanalyzed Ġfor Ġthe Ġpresence Ġof Ġthis Ġkey - word . </s> Response ĠGeneration : ĠIn Ġthis Ġmodule , Ġthe Ġsemantic Ġrepresentation Ġof Ġthe Ġsystem 's Ġnext Ġaction Ġis Ġtransformed Ġinto Ġnatural Ġlanguage . ĠHence , Ġeach Ġpossible Ġaction Ġis Ġmapped Ġto Ġprecisely Ġone Ġutter ance , Ġwhich Ġis Ġstored Ġin Ġthe Ġtemplates . ĠSome Ġof Ġthe Ġpred efined Ġresponses Ġare Ġfixed Ġ( i . e ., ĠâĢ ľ W el ches ĠKap it el Ġbear beit est Ġdu Ġger ade ? âĢ Ŀ ), Ġothers Ġhave Ġplace holders Ġfor Ġsystem Ġvalues . ĠIn Ġthe Ġlatter Ġcase , Ġthe Ġutter ance Ġcan Ġbe Ġformulated Ġdependent Ġon Ġthe Ġactual ĠID . ĠThe Ġdialogue Ġshowcases Ġcan Ġbe Ġfound Ġin ĠSection ĠSEC REF 14 Ġof Ġthe ĠAppendix . </s> E val uation </s> In Ġorder Ġto Ġget Ġthe Ġfeedback Ġon Ġthe Ġquality , Ġfunctionality , Ġand Ġusefulness Ġof Ġthe Ġintroduced Ġmodel , Ġwe Ġevaluated Ġit Ġin Ġtwo Ġways : Ġfirst , Ġwith Ġan Ġautomated Ġmethod Ġusing Ġ130 Ġmanually Ġannot ated Ġdialog ues , Ġto Ġprove Ġthe Ġrobust ness Ġof Ġthe Ġsystem , Ġand Ġsecond ĠâĢĵ Ġwith Ġhuman Ġtut ors Ġfrom ĠO MB + ĠâĢĵ Ġto Ġinvestigate Ġthe Ġuser Ġexperience . ĠWe Ġdescribe Ġthe Ġdetails Ġas Ġwell Ġas Ġthe Ġmost Ġcommon Ġerrors Ġbelow . </s> E val uation Ġ: :: ĠAutom ated ĠEvaluation </s> To Ġconduct Ġan Ġautomated Ġevaluation , Ġwe Ġmanually Ġcreated Ġa Ġdataset Ġwith Ġ130 Ġdialog Ġscenarios . ĠWe Ġtook Ġreal Ġfirst Ġuser Ġquestions Ġand Ġpred efined Ġpossible Ġuser Ġresponses , Ġas Ġwell Ġas Ġgold Ġsystem Ġanswers . ĠThese Ġscenarios Ġcover Ġmost Ġfrequent Ġdialog ues Ġthat Ġwe Ġpreviously Ġsaw Ġin Ġhuman - to - human Ġdialogue Ġdata . ĠWe Ġtested Ġour Ġsystem Ġby Ġimplementing Ġa Ġself - chat ting Ġevaluation Ġbot . ĠThe Ġevaluation Ġcycle Ġcould Ġbe Ġdescribed Ġas Ġfollows : </s> The Ġsystem Ġreceives Ġthe Ġfirst Ġquestion Ġfrom Ġa Ġpred efined Ġdialogue Ġvia Ġan ĠAPI Ġrequest , Ġpre process es Ġand Ġanaly zes Ġit Ġin Ġorder Ġto Ġextract Ġentities . </s> Then Ġit Ġestimates Ġthe Ġapplicable Ġnext Ġaction , Ġand Ġresponds Ġaccording Ġto Ġit . </s> This Ġresponse Ġis Ġthen Ġcompared Ġto Ġthe Ġsystem 's Ġgold Ġanswer : ĠIf Ġthe Ġpredicted Ġanswer Ġis Ġcorrect , Ġthen Ġthe Ġsystem Ġreceives Ġthe Ġnext Ġpred efined Ġuser Ġinput Ġfrom Ġthe Ġdialog Ġand Ġresponds Ġagain , Ġas Ġdefined Ġabove . ĠThis Ġprocedure Ġcontinues Ġuntil Ġthe Ġdialog Ġtermin ates Ġ( i . e ., ĠID Ġis Ġcomplete ). ĠOtherwise , Ġthe Ġsystem Ġfails , Ġreporting Ġthe Ġunsuccessful Ġcase Ġnumber . </s> Our Ġfinal Ġsystem Ġsuccessfully Ġpassed Ġthis Ġevaluation Ġfor Ġall Ġ130 Ġcases . </s> E val uation Ġ: :: ĠHuman ĠEvaluation Ġand ĠError ĠAnalysis </s> To Ġevaluate Ġour Ġsystem Ġon Ġirregular Ġexamples , Ġwe Ġconducted Ġexperiments Ġwith Ġhuman Ġtut ors Ġfrom Ġthe ĠO MB + Ġplatform . ĠThe Ġtut ors Ġare Ġexperienced Ġregarding Ġrare Ġor Ġcomplex Ġquestions , Ġambiguous Ġanswers , Ġmiss pell ings Ġand Ġother Ġinf requent Ġbut Ġstill Ġvery Ġrelevant Ġproblems , Ġwhich Ġoccur Ġduring Ġa Ġdialogue . ĠIn Ġthe Ġfollowing , Ġwe Ġinvestigate Ġsome Ġcommon Ġerrors Ġand Ġmake Ġadditional Ġobservations . </s> Miss pell ings Ġand Ġconf usable Ġspell ings Ġoccur Ġquite Ġoften Ġin Ġthe Ġuser - generated Ġtext , Ġand Ġsince Ġwe Ġattempt Ġto Ġlet Ġthe Ġconversation Ġremain Ġvery Ġnatural Ġfrom Ġthe Ġuser Ġside Ġand Ġthus , Ġcannot Ġrequire Ġformal Ġwriting , Ġwe Ġhave Ġto Ġdeal Ġwith Ġvarious Ġwriting Ġissues . ĠOne Ġof Ġthe Ġmost Ġfrequent Ġproblems Ġis Ġmiss pell ings . ĠGerman Ġwords Ġare Ġgenerally Ġlong Ġand Ġcan Ġbe Ġcomplicated , Ġand Ġsince Ġusers Ġtype Ġquickly , Ġthis Ġoften Ġleads Ġto Ġthe Ġwrong Ġorder Ġof Ġcharacters Ġwithin Ġwords . ĠTo Ġtackle Ġthis Ġchallenge , Ġwe Ġused Ġfuzzy Ġmatch Ġwithin ĠES . ĠHowever , Ġthe Ġmaximum Ġallowed Ġedit Ġdistance Ġin ĠElastic search Ġis Ġset Ġto Ġ2 Ġcharacters . ĠThis Ġmeans , Ġthat Ġall Ġthe Ġmiss pell ings Ġbeyond Ġthis Ġthreshold Ġcould Ġnot Ġbe Ġaccurately Ġrecognized Ġby ĠES Ġ( e . g ., ĠDifferent ial re chn ung Ġvs ĠDiff er ne z ial re chn ung ). ĠAnother Ġcharacteristic Ġexample Ġwould Ġbe Ġthe Ġwriting Ġof Ġthe Ġsection Ġor Ġquestion Ġnumber . ĠThe Ġequivalent Ġinformation Ġcan Ġbe Ġwritten Ġin Ġseveral Ġdistinct Ġways , Ġwhich Ġhas Ġto Ġbe Ġconsidered Ġin Ġour ĠReg Ex Ġunit Ġ( e . g ., ĠExercise Ġ5 Ġa , ĠExercise ĠV Ġa , ĠExercise Ġ5 Ġ( a )). ĠA Ġsimilar Ġproblem Ġoccurs Ġwith Ġconf usable Ġspelling Ġ( i . e .: ĠDifferent ial re chn ung Ġvs ĠDifferent ial g le ich ung ). ĠWe Ġanalyzed Ġthe Ġcases Ġmentioned Ġabove Ġand Ġadded Ġsome Ġof Ġthe Ġmost Ġcommon Ġissues Ġto Ġthe ĠES Ġdatabase Ġor Ġhandled Ġthem Ġwith ĠReg Ex Ġduring Ġthe Ġpre processing Ġstep . </s> El astic search ĠTh reshold : ĠIn Ġsome Ġcases , Ġthe Ġsystem Ġfailed Ġto Ġextract Ġinformation , Ġalthough Ġthe Ġuser Ġprovided Ġit . ĠIn Ġother Ġcases , ĠES Ġextracts Ġinformation Ġthat Ġwas Ġnot Ġmentioned Ġin Ġa Ġuser Ġquery Ġat Ġall . ĠThat Ġoccurs Ġdue Ġto Ġthe Ġrele v ancy Ġscoring Ġalgorithm Ġof ĠElastic search , Ġwhere Ġa Ġdocument 's Ġscore Ġis Ġa Ġcombination Ġof Ġtextual Ġsimilarity Ġand Ġother Ġmetadata Ġbased Ġscores . ĠOur Ġanalysis Ġrevealed Ġthat ĠES Ġmostly Ġfails Ġto Ġextract Ġthe Ġinformation Ġif Ġthe Ġsentence Ġ( i . e ., Ġuser Ġmessage ) Ġis Ġquite Ġshort Ġ( e . g ., Ġ5 Ġwords ). ĠTo Ġovercome Ġthis Ġdifficulty , Ġwe Ġcombined Ġthe Ġcurrent Ġinput Ġ$ u _{ t } $ Ġwith Ġthe Ġdialog Ġhistory . ĠThis Ġstep Ġeliminated Ġthe Ġproblem Ġand Ġimproved Ġthe Ġretrieval Ġquality . ĠTo Ġsolve Ġthe Ġcase Ġwhere ĠElastic search Ġextracts Ġincorrect Ġinformation Ġ( or Ġinformation Ġthat Ġwas Ġnot Ġmentioned Ġin Ġa Ġquery ) Ġis Ġmore Ġchallenging . ĠWe Ġdiscovered Ġthat Ġthe Ġproblem Ġcomes Ġfrom Ġshort Ġwords Ġor Ġsub - words Ġ( e . g ., Ġsuffix es , Ġprefix es ), Ġwhich ĠES Ġconsiders Ġto Ġbe Ġcredible Ġenough . ĠThe ĠElastic search Ġdocumentation Ġsuggests Ġgetting Ġrid Ġof Ġstop Ġwords Ġto Ġeliminate Ġthis Ġbehavior . ĠHowever , Ġthis Ġdid Ġnot Ġimprove Ġthe Ġsearch Ġin Ġour Ġcase . ĠAlso , Ġfine - tun ing Ġof ĠES Ġparameters Ġsuch Ġas Ġthe Ġrelevance Ġthreshold , Ġprefix Ġlength Ġand Ġminimum Ġshould Ġmatch Ġparameter Ġdid Ġnot Ġbring Ġsignificant Ġimprovements . ĠTo Ġcope Ġwith Ġthis Ġproblem , Ġwe Ġimplemented Ġa Ġverification Ġstep , Ġwhere Ġa Ġuser Ġis Ġgiven Ġa Ġchance Ġto Ġcorrect Ġthe Ġerrone ously Ġretrieved Ġinformation . </s> The Ġoverall Ġfeedback Ġfrom Ġthe Ġtut ors Ġincluded Ġreduced Ġrepetitive Ġactivities Ġas Ġwell Ġas Ġreduced Ġwaiting Ġtimes Ġfor Ġstudents Ġuntil Ġtheir Ġquestions Ġwere Ġprocessed . ĠAlso , Ġtut ors Ġreported Ġthat Ġthe Ġrate Ġof Ġcancelled Ġsessions Ġ( sw itching Ġto Ġa Ġhuman Ġtutor ) Ġis Ġrather Ġlow . </s> Struct ured ĠDialogue ĠAcquisition </s> As Ġwe Ġalready Ġmentioned , Ġour Ġsystem Ġattempts Ġto Ġsupport Ġthe Ġhuman Ġtutor Ġby Ġassisting Ġstudents , Ġbut Ġit Ġalso Ġcollects Ġstructured Ġand Ġlabeled Ġtraining Ġdata Ġin Ġthe Ġbackground . ĠIn Ġa Ġtrial Ġrun Ġof Ġthe Ġrule - based Ġsystem , Ġwe Ġwere Ġable Ġto Ġaccumulate Ġa Ġtoy - dat as et Ġwith Ġtraining Ġdialog ues . ĠThe Ġassembled Ġdialog ues Ġhave Ġthe Ġfollowing Ġformat : </s> Pl ain Ġdialog ues Ġwith Ġunique Ġdialogue Ġindexes ; </s> Pl ain ĠInformation ĠDictionary Ġinformation Ġ( e . g ., Ġextracted Ġentities ) Ġcollected Ġfor Ġthe Ġwhole Ġdialogue ; </s> P airs Ġof Ġquestions Ġ( i . e ., Ġuser Ġrequests ) Ġand Ġresponses Ġ( i . e ., Ġbot Ġresponses ) Ġwith Ġthe Ġunique Ġdialogue - Ġand Ġturn - index es ; </s> Tri ples Ġin Ġthe Ġform Ġof Ġ( User ĠRequest , ĠNext ĠAction , ĠResponse ). ĠInformation Ġon Ġthe Ġnext Ġsystem 's Ġaction Ġcould Ġbe Ġemployed Ġto Ġtrain Ġa ĠDialogue ĠManager Ġunit Ġwith Ġ( deep -) Ġmachine Ġlearning Ġalgorithms ; </s> For Ġeach Ġstate Ġin Ġthe Ġdialogue , Ġwe Ġsaved Ġthe Ġentities Ġthat Ġthe Ġsystem Ġwas Ġable Ġto Ġextract Ġfrom Ġthe Ġprovided Ġuser Ġquery , Ġalong Ġwith Ġtheir Ġposition Ġin Ġthe Ġutter ance . ĠThis Ġinformation Ġcould Ġbe Ġused Ġto Ġtrain Ġa Ġcustom , Ġdomain Ġspecific ĠNamed ĠEntity ĠRecogn ition Ġmodel . </s> Re - im plementation Ġof Ġunits Ġwith ĠB ERT </s> As Ġwe Ġmentioned Ġbefore , Ġthere Ġare Ġmany Ġcases , Ġespecially Ġin Ġthe Ġindustry , Ġwhere Ġthe Ġlabeled Ġand Ġstructured Ġdata Ġis Ġnot Ġdirectly Ġavailable . ĠCollect ing Ġand Ġlabeling Ġsuch Ġdata Ġis Ġoften Ġa Ġtedious Ġand Ġtime - consuming Ġtask . ĠThus , Ġalgorithms Ġthat Ġenable Ġtraining Ġof Ġthe Ġsystems Ġwith Ġless Ġor Ġeven Ġa Ġminimal Ġamount Ġof Ġdata Ġare Ġhighly Ġrequired . ĠSuch Ġalgorithms Ġcan Ġtransfer Ġthe Ġknowledge Ġobtained Ġduring Ġthe Ġtraining Ġon Ġexisting Ġdata Ġto Ġthe Ġunseen Ġdomain . ĠThey Ġare , Ġtherefore , Ġone Ġof Ġthe Ġpotential Ġsolutions Ġfor Ġindustrial Ġproblems . </s> Once Ġwe Ġassembled Ġa Ġdataset Ġof Ġstructured Ġdata Ġvia Ġour Ġrule - based Ġsystem , Ġwe Ġre - im ple mented Ġtwo Ġout Ġof Ġthree Ġcentral Ġdialogue Ġcomponents Ġin Ġour Ġconvers ational Ġassistant Ġwith Ġdeep Ġlearning Ġmethods . ĠSince Ġthe Ġavailable Ġdata Ġwas Ġcollected Ġin Ġa Ġtrial - run Ġand Ġthus Ġthe Ġobtained Ġdataset Ġwas Ġrather Ġsmall Ġto Ġtrain Ġa Ġmachine Ġlearning Ġmodel Ġfrom Ġscratch , Ġwe Ġutilized Ġthe ĠTransfer ĠLearning Ġapproach , Ġand Ġfine - tun ed Ġthe Ġexisting Ġpre - trained Ġmodel Ġ( i . e ., ĠB ERT ) Ġfor Ġour Ġtarget Ġdomain Ġand Ġdata . </s> For Ġthe Ġexperiments , Ġwe Ġdefined Ġtwo Ġtasks : </s> First , Ġwe Ġstudied Ġthe ĠNamed ĠEntity ĠRecogn ition Ġproblem Ġin Ġa Ġcustom Ġdomain Ġsetting . ĠWe Ġdefined Ġa Ġsequence Ġlabeling Ġtask Ġand Ġemployed Ġthe ĠB ERT Ġmodel ĠB IB REF 4 . ĠWe Ġapplied Ġthe Ġmodel Ġto Ġour Ġdataset Ġand Ġfine - tun ed Ġit Ġfor Ġsix Ġ( 6 ) Ġdomain - specific Ġ( i . e ., Ġe - learning ) Ġentities Ġand Ġone Ġ( 1 ) ĠâĢ ľ unknown âĢ Ŀ Ġlabel . </s> Second , Ġwe Ġinvestigated Ġthe Ġeffectiveness Ġof ĠB ERT Ġfor Ġthe Ġdialogue Ġmanager Ġcore . ĠFor Ġthat Ġexperiment , Ġwe Ġdefined Ġa Ġclassification Ġtask Ġand Ġapplied Ġthe Ġmodel Ġto Ġpredict Ġthe Ġsystem 's ĠNext ĠAction Ġfor Ġevery Ġgiven Ġuser Ġutter ance Ġin Ġa Ġconversation . ĠWe Ġthen Ġcomputed Ġthe Ġmacro ĠF - score Ġfor Ġ13 Ġpossible Ġactions Ġand Ġan Ġaverage Ġdialogue Ġaccuracy . </s> Finally , Ġwe Ġverified Ġthat Ġthe Ġapplied Ġmodel Ġperformed Ġwell Ġon Ġboth Ġtasks : ĠWe Ġachieved Ġthe Ġperformance Ġof Ġ$ 0 . 93 $ Ġmacro ĠF 1 Ġpoints Ġfor ĠNamed ĠEntity ĠRecogn ition Ġ( NER ) Ġand Ġ$ 0 . 75 $ Ġmacro ĠF 1 Ġpoints Ġfor Ġthe ĠNext ĠAction ĠPrediction Ġ( N AP ) Ġtask . ĠWe , Ġtherefore , Ġconclude Ġthat Ġboth ĠN ER Ġand ĠN AP Ġcomponents Ġcould Ġbe Ġemployed Ġto Ġsubstitute Ġor Ġextend Ġthe Ġexisting Ġrule - based Ġmodules . </s> Data Ġ& ĠDes cript ive ĠStatistics : ĠThe Ġdataset Ġthat Ġwe Ġcollected Ġduring Ġthe Ġtrial - run Ġconsists Ġof Ġ300 Ġstructured Ġdialog ues Ġwith Ġthe Ġaverage Ġlength Ġof Ġa Ġdialogue Ġbeing Ġsix Ġ( 6 ) Ġutter ances . ĠCommunication Ġwith Ġstudents Ġwas Ġperformed Ġin Ġthe ĠGerman Ġlanguage . ĠDet ailed Ġgeneral Ġstatistics Ġcan Ġbe Ġfound Ġin ĠTable ĠT AB REF 41 . </s> N amed ĠEntity ĠRecogn ition : ĠWe Ġdefined Ġa Ġsequence Ġlabeling Ġtask Ġto Ġextract Ġcustom Ġentities Ġfrom Ġuser Ġinput . ĠWe Ġassumed Ġseven Ġ( 7 ) Ġpossible Ġentities Ġ( see ĠTable ĠT AB REF 43 ) Ġto Ġbe Ġrecognized Ġby Ġthe Ġmodel : Ġtopic , Ġsub topic , Ġexamination Ġmode Ġand Ġlevel , Ġquestion Ġnumber , Ġintent , Ġas Ġwell Ġas Ġthe Ġentity Ġother Ġfor Ġremaining Ġwords Ġin Ġthe Ġutter ance . ĠSince Ġthe Ġdata Ġobtained Ġfrom Ġthe Ġrule - based Ġsystem Ġalready Ġcontains Ġinformation Ġon Ġthe Ġentities Ġextracted Ġfrom Ġeach Ġuser Ġquery Ġ( i . e ., Ġby Ġmeans Ġof ĠElastic search ), Ġwe Ġcould Ġuse Ġit Ġto Ġtrain Ġa Ġdomain - specific ĠN ER Ġunit . ĠHowever , Ġsince Ġthe Ġuser - input Ġwas Ġinformal , Ġthe Ġsame Ġinformation Ġcould Ġbe Ġprovided Ġin Ġdifferent Ġwriting Ġstyles . ĠThat Ġmeans Ġthat Ġa Ġsingle Ġentity Ġcould Ġhave Ġdifferent Ġsurface Ġforms Ġ( e . g ., Ġsyn onyms , Ġwriting Ġstyles ) Ġ( although Ġentities Ġthat Ġwe Ġextracted Ġfrom Ġthe Ġrule - based Ġsystem Ġwere Ġall Ġconverted Ġto Ġa Ġuniversal Ġstandard , Ġe . g ., Ġofficial Ġchapter Ġnames ). ĠTo Ġconsider Ġall Ġof Ġthe Ġvariable Ġentity Ġforms Ġwhile Ġpost - label ing Ġthe Ġoriginal Ġdataset , Ġwe Ġdefined Ġgeneric Ġentity Ġnames Ġ( e . g ., Ġchapter , Ġquestion Ġn r .) Ġand Ġmapped Ġvariations Ġof Ġentities Ġfrom Ġthe Ġuser Ġinput Ġ( e . g ., ĠChapter Ġ= Ġ[ Element ary ĠCal culus , ĠChapter Ġ$ I $, Ġ... ]) Ġto Ġthem . </s> Next ĠAction ĠPrediction : ĠWe Ġdefined Ġa Ġclassification Ġproblem Ġto Ġpredict Ġthe Ġsystem 's Ġnext Ġaction Ġaccording Ġto Ġthe Ġgiven Ġuser Ġinput . ĠWe Ġassumed Ġ13 Ġcustom Ġactions Ġ( see ĠTable ĠT AB REF 42 ) Ġthat Ġwe Ġconsidered Ġbeing Ġour Ġlabels . ĠIn Ġthe Ġconvers ational Ġdataset , Ġeach Ġinput Ġwas Ġautomatically Ġlabeled Ġby Ġthe Ġrule - based Ġsystem Ġwith Ġthe Ġcorresponding Ġnext Ġaction Ġand Ġthe Ġdialogue - id . ĠThus , Ġno Ġadditional Ġpost - label ing Ġwas Ġrequired . ĠWe Ġinvestigated Ġtwo Ġsettings : </s> Default ĠSetting : ĠUsing Ġonly Ġa Ġuser Ġinput Ġand Ġthe Ġcorresponding Ġlabel Ġ( i . e ., Ġnext Ġaction ) Ġwithout Ġadditional Ġcontext . ĠBy Ġdefault , Ġwe Ġrun Ġall Ġof Ġour Ġexperiments Ġin Ġthis Ġsetting . </s> Ext ended ĠSetting : ĠUsing Ġa Ġuser Ġinput , Ġa Ġcorresponding Ġnext Ġaction , Ġand Ġa Ġprevious Ġsystem Ġaction Ġas Ġa Ġsource Ġof Ġadditional Ġcontext . ĠFor Ġthis Ġsetting , Ġwe Ġrun Ġan Ġexperiment Ġwith Ġthe Ġbest Ġperforming Ġmodel Ġfrom Ġthe Ġdefault Ġsetting . </s> The Ġoverall Ġdataset Ġconsists Ġof Ġ300 Ġlabeled Ġdialog ues , Ġwhere Ġ200 Ġ( with Ġ11 61 Ġutter ances ) Ġof Ġthem Ġwere Ġemployed Ġfor Ġtraining , Ġand Ġ100 Ġfor Ġevaluation Ġand Ġtest Ġsets Ġ( 50 Ġdialog ues Ġwith Ġabout Ġ300 Ġutter ances Ġfor Ġeach Ġset Ġrespectively ). </s> Model ĠSettings : ĠFor Ġthe ĠN ER Ġtask Ġwe Ġconducted Ġexperiments Ġwith ĠGerman Ġand Ġmult ilingual ĠB ERT Ġimplementations . ĠSince Ġin Ġthe ĠGerman Ġlanguage Ġthe Ġcapital ization Ġof Ġwords Ġplays Ġa Ġsignificant Ġrole , Ġwe Ġrun Ġour Ġtests Ġon Ġthe Ġcapital ized Ġinput , Ġwhile Ġkeeping Ġthe Ġoriginal Ġpunct uation . ĠHence , Ġwe Ġemployed Ġthe Ġavailable Ġbase Ġmodel Ġfor Ġboth Ġmult ilingual Ġand ĠGerman ĠB ERT Ġimplementations Ġin Ġthe Ġc ased Ġversion . ĠWe Ġset Ġthe Ġlearning Ġrate Ġfor Ġboth Ġmodels Ġto Ġ$ 1 e - 4 $ Ġand Ġthe Ġmaximum Ġlength Ġof Ġthe Ġtoken ized Ġinput Ġwas Ġset Ġto Ġ128 Ġtokens . ĠWe Ġrun Ġthe Ġexperiments Ġmultiple Ġtimes Ġwith Ġdifferent Ġseeds Ġfor Ġa Ġmaximum Ġof Ġ50 Ġepoch s Ġwith Ġthe Ġtraining Ġbatch Ġsize Ġset Ġto Ġ32 . ĠWe Ġutilized ĠAdam W Ġas Ġthe Ġoptim izer Ġand Ġemployed Ġearly Ġstopping , Ġif Ġthe Ġperformance Ġdid Ġnot Ġchange Ġsignificantly Ġafter Ġ5 Ġepoch s . </s> For Ġthe ĠN AP Ġtask Ġwe Ġconducted Ġexperiments Ġwith ĠGerman Ġand Ġmult ilingual ĠB ERT Ġimplementations Ġas Ġwell . ĠHere , Ġwe Ġinvestigated Ġthe Ġperformance Ġof Ġboth Ġcapital ized Ġand Ġlower c ased Ġinput , Ġas Ġwell Ġas Ġplain Ġand Ġpre process ed Ġdata . ĠFor Ġthe Ġmult ilingual ĠB ERT , Ġwe Ġemployed Ġthe Ġbase Ġmodel Ġin Ġboth Ġc ased Ġand Ġunc ased Ġvariations . ĠFor Ġthe ĠGerman ĠB ERT , Ġwe Ġutilized Ġthe Ġbase Ġmodel Ġin Ġthe Ġc ased Ġvariation Ġonly . ĠFor Ġboth Ġmodels , Ġwe Ġset Ġthe Ġlearning Ġrate Ġto Ġ$ 4 e - 5 $, Ġand Ġthe Ġmaximum Ġlength Ġof Ġthe Ġtoken ized Ġinput Ġwas Ġset Ġto Ġ128 Ġtokens . ĠWe Ġrun Ġthe Ġexperiments Ġmultiple Ġtimes Ġwith Ġdifferent Ġseeds Ġfor Ġa Ġmaximum Ġof Ġ300 Ġepoch s Ġwith Ġthe Ġtraining Ġbatch Ġsize Ġset Ġto Ġ32 . ĠWe Ġutilized ĠAdam W Ġas Ġthe Ġoptim izer Ġand Ġemployed Ġearly Ġstopping , Ġif Ġthe Ġperformance Ġdid Ġnot Ġchange Ġsignificantly Ġafter Ġ15 Ġepoch s . </s> E val uation Ġand ĠDiscussion : ĠFor Ġthe Ġevaluation , Ġwe Ġcomputed Ġword - level Ġmacro ĠF 1 Ġscore Ġfor Ġthe ĠN ER Ġtask Ġand Ġutter ance - level Ġmacro ĠF 1 Ġscore Ġfor Ġthe ĠN AP Ġtask . ĠThe Ġword - level ĠF 1 Ġis Ġestimated Ġas Ġthe Ġaverage Ġof Ġthe ĠF 1 Ġscores Ġper Ġclass , Ġeach Ġcomputed Ġfrom Ġall Ġwords Ġin Ġthe Ġevaluation Ġand Ġtest Ġsets . ĠThe Ġresults Ġfor Ġthe ĠN ER Ġtask Ġare Ġdepicted Ġin ĠTable ĠT AB REF 49 . ĠFor Ġutter ance - level ĠF 1 , Ġa Ġsingle Ġlabel Ġ( i . e ., Ġnext Ġaction ) Ġis Ġobtained Ġfor Ġthe Ġwhole Ġutter ance . ĠThe Ġresults Ġfor Ġthe ĠN AP Ġtask Ġare Ġpresented Ġin ĠTable ĠT AB REF 48 . ĠWe Ġadditionally Ġcomputed Ġaverage Ġdialogue Ġaccuracy Ġfor Ġthe Ġbest Ġperforming ĠN AP Ġmodels . ĠThis Ġscore Ġdenotes Ġhow Ġwell Ġthe Ġpredicted Ġnext Ġactions Ġmatch Ġthe Ġgold Ġnext Ġactions Ġand Ġthus Ġform Ġthe Ġdialogue Ġflow Ġwithin Ġeach Ġconversation . ĠThe Ġaverage Ġdialogue Ġaccuracy Ġwas Ġcomputed Ġfor Ġ50 Ġdialog ues Ġin Ġthe Ġevaluation Ġand Ġtest Ġsets Ġrespectively . ĠThe Ġresults Ġare Ġdisplayed Ġin ĠTable ĠT AB REF 50 . </s> The Ġobtained Ġresults Ġfor Ġthe ĠN ER Ġtask Ġrevealed Ġthat ĠGerman ĠB ERT Ġperformed Ġsignificantly Ġbetter Ġthan Ġthe Ġmult ilingual ĠB ERT Ġmodel . ĠThe Ġperformance Ġof Ġthe Ġcustom ĠN ER Ġunit Ġis Ġat Ġ$ 0 . 93 $ Ġmacro ĠF 1 Ġpoints Ġfor Ġall Ġpossible Ġnamed Ġentities Ġ( see ĠTable ĠT AB REF 49 ). ĠIn Ġcontrast , Ġfor Ġthe ĠN AP Ġtask , Ġthe Ġmult ilingual ĠB ERT Ġmodel Ġobtained Ġbetter Ġperformance Ġthan Ġthe ĠGerman ĠB ERT Ġmodel . ĠHere , Ġthe Ġbest Ġperforming Ġsystem Ġin Ġthe Ġdefault Ġsetting Ġachieved Ġa Ġmacro ĠF 1 Ġof Ġ$ 0 . 677 $ Ġpoints Ġfor Ġ14 Ġpossible Ġlabels , Ġwhereas Ġthe Ġmodel Ġin Ġthe Ġextended Ġsetting Ġperformed Ġbetter ĠâĢĵ Ġits Ġhighest Ġmacro ĠF 1 Ġscore Ġis Ġ$ 0 . 752 $ Ġfor Ġthe Ġsame Ġamount Ġof Ġlabels Ġ( see ĠTable ĠT AB REF 48 ). ĠConsidering Ġthe Ġdialogue Ġaccuracy , Ġthe Ġextended Ġsystem Ġtrained Ġwith Ġmult ilingual ĠB ERT Ġachieved Ġbetter Ġresults Ġthan Ġthe Ġdefault Ġone Ġwith Ġ$ 0 . 801 $ Ġaccuracy Ġpoints Ġcompared Ġto Ġ$ 0 . 7 24 $ Ġaccuracy Ġpoints Ġfor Ġthe Ġtest Ġset Ġ( see ĠTable ĠT AB REF 50 ). ĠThe Ġoverall Ġobservation Ġfor Ġthe ĠN AP Ġis Ġthat Ġthe Ġcapital ized Ġsetting Ġimproved Ġthe Ġperformance Ġof Ġthe Ġmodel , Ġwhereas Ġthe Ġinclusion Ġof Ġpunct uation Ġhas Ġnot Ġpositively Ġinfluenced Ġthe Ġresults . </s> Error ĠAnalysis : </s> After Ġthe Ġevaluation Ġstep , Ġwe Ġanalyzed Ġthe Ġcases , Ġwhere Ġthe Ġmodel Ġfailed Ġto Ġpredict Ġthe Ġcorrect Ġaction Ġor Ġlabeled Ġthe Ġnamed Ġentity Ġspan Ġerrone ously . ĠBelow Ġwe Ġdescribe Ġthe Ġmost Ġcommon Ġerrors Ġfor Ġboth Ġtasks . </s> Next ĠAction ĠPrediction : ĠOne Ġof Ġthe Ġmost Ġfrequent Ġerrors Ġin Ġthe Ġdefault Ġmodel Ġwas Ġthe Ġmismatch Ġbetween Ġtwo Ġconsecutive Ġactions ĠâĢĵ Ġnamely , Ġthe Ġaction ĠQuestion ĠNumber Ġand ĠSub topic . ĠThat Ġis Ġdue Ġto Ġthe Ġorder Ġof Ġthese Ġactions Ġin Ġthe Ġconvers ational Ġflow : ĠOcc urrence Ġof Ġboth Ġactions Ġin Ġthe Ġdialogue Ġis Ġnot Ġstrict Ġand Ġsubstantially Ġdepends Ġon Ġthe Ġprevious Ġsystem Ġaction . ĠHowever , Ġthe Ġanalysis Ġof Ġthe Ġextended Ġmodel Ġrevealed Ġthat Ġthe Ġintroduction Ġof Ġadditional Ġcontext Ġin Ġthe Ġform Ġof Ġthe Ġprevious Ġaction Ġimproved Ġthe Ġperformance Ġof Ġthe Ġsystem Ġin Ġthis Ġparticular Ġcase Ġby Ġabout Ġ$ 60 \ % $. </s> N amed ĠEntity ĠRecogn ition : ĠThe Ġfailing Ġcases Ġinclude Ġmism atches Ġbetween Ġthe Ġtags ĠâĢ ľ chapter âĢ Ŀ Ġand ĠâĢ ľ other âĢ Ŀ , Ġand Ġthe Ġtags ĠâĢ ľ question Ġnumber âĢ Ŀ Ġand ĠâĢ ľ other âĢ Ŀ . ĠThis Ġtype Ġof Ġerror Ġarose Ġdue Ġto Ġthe Ġimperfect ly Ġlabeled Ġspan Ġof Ġa Ġmulti - word Ġnamed Ġentity . ĠIn Ġsuch Ġcases , Ġthe Ġfirst Ġor Ġlast Ġword Ġin Ġthe Ġnamed Ġentity Ġwas Ġexcluded Ġfrom Ġthe Ġspan Ġand Ġerrone ously Ġlabeled Ġwith Ġthe Ġtag ĠâĢ ľ other âĢ Ŀ . </s> Related ĠWork </s> Individual Ġcomponents Ġof Ġa Ġparticular Ġdialogue Ġsystem Ġcould Ġbe Ġimplemented Ġusing Ġa Ġdifferent Ġkind Ġof Ġapproach , Ġstarting Ġwith Ġentirely Ġrule - Ġand Ġtemplate - based Ġmethods , Ġand Ġgoing Ġtowards Ġhybrid Ġapproaches Ġ( using Ġlearn able Ġcomponents Ġalong Ġwith Ġhand crafted Ġunits ) Ġand Ġend - to - end Ġtrain able Ġmachine Ġlearning Ġmethods . </s> Rule - based ĠAppro aches : ĠThough Ġmany Ġof Ġthe Ġlatest Ġresearch Ġapproaches Ġhandle ĠNL U Ġand ĠNL G Ġunits Ġby Ġusing Ġstatistical ĠN LP Ġmodels ĠB IB REF 5 , ĠB IB REF 6 , ĠB IB REF 7 , Ġmost Ġof Ġthe Ġindust ri ally Ġdeployed Ġdialogue Ġsystems Ġstill Ġuse Ġmanual Ġfeatures Ġor Ġhand crafted Ġrules Ġfor Ġthe Ġstate Ġand Ġaction Ġprediction , Ġintent Ġclassification , Ġand Ġslot Ġfilling Ġtasks ĠB IB REF 8 , ĠB IB REF 9 . ĠThe Ġrule - based Ġapproach Ġensures Ġrobust ness Ġand Ġstable Ġperformance Ġthat Ġis Ġcrucial Ġfor Ġindustrial Ġsystems Ġthat Ġinteract Ġwith Ġa Ġlarge Ġnumber Ġof Ġusers Ġsimultaneously . ĠHowever , Ġit Ġis Ġhighly Ġexpensive Ġand Ġtime - consuming Ġto Ġdeploy Ġa Ġreal Ġdialogue Ġsystem Ġbuilt Ġin Ġthis Ġmanner . ĠThe Ġmajor Ġdisadvantage Ġis Ġthat Ġthe Ġusage Ġof Ġhand crafted Ġsystems Ġis Ġrestricted Ġto Ġa Ġspecific Ġdomain , Ġand Ġpossible Ġdomain Ġadaptation Ġrequires Ġextensive Ġmanual Ġengineering . </s> End - to - End ĠLearning ĠAppro aches : ĠDue Ġto Ġthe Ġrecent Ġadvance Ġof Ġend - to - end Ġneural Ġgener ative Ġmodels ĠB IB REF 10 , Ġmany Ġefforts Ġhave Ġbeen Ġmade Ġto Ġbuild Ġan Ġend - to - end Ġtrain able Ġarchitecture Ġfor Ġdialogue Ġsystems . ĠRather Ġthan Ġusing Ġthe Ġtraditional Ġpipeline , Ġan Ġend - to - end Ġmodel Ġis Ġconceived Ġas Ġa Ġsingle Ġmodule ĠB IB REF 8 . ĠDespite Ġhaving Ġbetter Ġadapt ability Ġcompared Ġto Ġany Ġrule - based Ġsystem Ġand Ġbeing Ġeasy Ġto Ġtrain , Ġend - to - end Ġapproaches Ġremain Ġunatt ain able Ġfor Ġcommercial Ġconvers ational Ġagents Ġoperating Ġon Ġreal - world Ġdata . ĠA Ġwell Ġand Ġcarefully Ġconstructed Ġtask - oriented Ġdialogue Ġsystem Ġin Ġa Ġknown Ġdomain Ġusing Ġhand crafted Ġrules Ġand Ġpred efined Ġresponses , Ġstill Ġoutper forms Ġthe Ġend - to - end Ġsystems Ġdue Ġto Ġits Ġrobust ness ĠB IB REF 11 , ĠB IB REF 12 . </s> Hy brid ĠAppro aches : ĠThough Ġend - to - end Ġlearning Ġis Ġan Ġattractive Ġsolution Ġfor Ġdialogue Ġsystems , Ġcurrent Ġtechniques Ġare Ġdata - intensive Ġand Ġrequire Ġlarge Ġamounts Ġof Ġdialog ues Ġto Ġlearn Ġsimple Ġactions . ĠTo Ġovercome Ġthis Ġdifficulty , ĠB IB REF 13 Ġ( BI B REF 13 ) Ġintroduce ĠHybrid ĠCode ĠNetworks Ġ( HC Ns ), Ġwhich Ġis Ġan Ġensemble Ġof Ġretrieval Ġand Ġtrain able Ġunits . ĠThe Ġauthors Ġreport , Ġthat Ġcompared Ġto Ġexisting Ġend - to - end Ġmethods , Ġtheir Ġapproach Ġconsiderably Ġreduces Ġthe Ġamount Ġof Ġdata Ġrequired Ġfor Ġtraining ĠB IB REF 13 . ĠHybrid Ġmodels Ġappear Ġto Ġreplace Ġthe Ġestablished Ġrule - Ġand Ġtemplate - based Ġapproaches Ġwhich Ġare Ġcurrently Ġutilized Ġin Ġan Ġindustrial Ġsetting . </s> Con clusions </s> In Ġthis Ġwork , Ġwe Ġimplemented Ġa Ġdialogue Ġsystem Ġfor ĠIntelligent ĠProcess ĠAutom ation Ġpurposes Ġthat Ġsimultaneously Ġsolves Ġtwo Ġproblems : ĠFirst , Ġit Ġreduces Ġrepetitive Ġand Ġtime - consuming Ġactivities Ġand , Ġtherefore , Ġallows Ġworkers Ġof Ġthe Ġe - learning Ġplatform Ġto Ġfocus Ġon Ġsolely Ġmathematical Ġand Ġhence Ġmore Ġcognitive ly Ġdemanding Ġquestions . ĠSecond , Ġby Ġinteracting Ġwith Ġusers , Ġit Ġaug ments Ġthe Ġresources Ġwith Ġstructured Ġand Ġlabeled Ġtraining Ġdata Ġfor Ġfurther Ġpossible Ġimplementation Ġof Ġlearn able Ġdialogue Ġcomponents . ĠThe Ġrealization Ġof Ġsuch Ġa Ġsystem Ġwas Ġconnected Ġwith Ġmany Ġchallenges . ĠAmong Ġothers Ġwere Ġmissing Ġstructured Ġdata , Ġambiguous Ġor Ġerroneous Ġuser - generated Ġtext Ġand Ġthe Ġnecessity Ġto Ġdeal Ġwith Ġalready Ġexisting Ġcorporate Ġtools Ġand Ġtheir Ġdesign . ĠThe Ġintroduced Ġmodel Ġallowed Ġus Ġto Ġaccumulate Ġstructured Ġand Ġto Ġsome Ġextent Ġlabeled Ġdata Ġwithout Ġany Ġspecial Ġefforts Ġfrom Ġthe Ġhuman Ġ( i . e ., Ġtut ors ) Ġside Ġ( e . g ., Ġmanual Ġannotation Ġof Ġexisting Ġdialog ues , Ġchange Ġof Ġthe Ġconvers ational Ġstructure ). ĠOnce Ġwe Ġcollected Ġstructured Ġdialog ues , Ġwe Ġwere Ġable Ġto Ġre - train Ġspecific Ġcomponents Ġof Ġthe Ġsystem Ġwith Ġdeep Ġlearning Ġmethods Ġand Ġachieved Ġreasonable Ġperformance Ġfor Ġall Ġproposed Ġtasks . </s> We Ġbelieve Ġthe Ġobtained Ġresults Ġare Ġrather Ġgood , Ġconsidering Ġa Ġrelatively Ġsmall Ġamount Ġof Ġdata Ġwe Ġutilized Ġto Ġfine - t une Ġthe Ġpre - trained Ġmodel . ĠWe , Ġtherefore , Ġconclude Ġthat Ġboth ĠNext ĠAction ĠPrediction Ġand ĠNamed ĠEntity ĠRecogn ition Ġcomponents Ġcould Ġbe Ġemployed Ġto Ġsubstitute Ġor Ġextend Ġthe Ġexisting Ġrule - based Ġmodules . ĠRule - based Ġunits Ġare Ġrestricted Ġin Ġtheir Ġcapabilities Ġand Ġcould Ġbe Ġhardly Ġadapt able Ġto Ġnovel Ġpatterns , Ġwhereas Ġthe Ġtrain able Ġunits Ġgeneral ize Ġbetter , Ġwhich Ġwe Ġbelieve Ġcould Ġreduce Ġthe Ġnumber Ġof Ġerroneous Ġpredictions Ġin Ġcase Ġof Ġunexpected Ġdialogue Ġbehavior . ĠFurthermore , Ġto Ġincrease Ġthe Ġoverall Ġrobust ness , Ġboth Ġrule - based Ġand Ġtrain able Ġcomponents Ġcould Ġbe Ġused Ġsynchron ously Ġas Ġa Ġhybrid Ġmodel : Ġin Ġcase Ġwhen Ġone Ġsystem Ġfails , Ġthe Ġdialogue Ġproceeds Ġon Ġthe Ġprediction Ġobtained Ġfrom Ġthe Ġother Ġmodel . </s> Future ĠWork </s> The Ġcore Ġof Ġthe Ġrule - based Ġmodel Ġis Ġa Ġdialogue Ġmanager Ġthat Ġdetermines Ġthe Ġcurrent Ġstate Ġof Ġthe Ġconversation Ġand Ġthe Ġpossible Ġnext Ġaction . ĠRule - based Ġsystems Ġare Ġgenerally Ġconsidered Ġto Ġbe Ġhardly Ġadapt able Ġto Ġnew Ġdomains ; Ġhowever , Ġour Ġdialogue Ġmanager Ġproved Ġto Ġbe Ġflexible Ġto Ġslight Ġmodifications Ġin Ġa Ġworkflow . ĠOne Ġof Ġthe Ġpossible Ġdirections Ġof Ġfuture Ġwork Ġwould Ġbe Ġthe Ġinvestigation Ġof Ġthe Ġgeneral Ġadapt ability Ġof Ġthe Ġdialogue Ġmanager Ġcore Ġto Ġother Ġscenarios Ġand Ġdomains Ġ( e . g ., Ġdifferent Ġcourse ). ĠFurther Ġinvestigation Ġcould Ġbe Ġtowards Ġthe Ġmulti - language Ġmod ality Ġfor Ġthe Ġre - im ple mented Ġunits . ĠSince Ġthe ĠO MB + Ġplatform Ġalso Ġsupports ĠEnglish Ġand ĠChinese , Ġit Ġwould Ġbe Ġinteresting Ġto Ġexamine Ġwhether Ġthe Ġsimple Ġtranslation Ġfrom Ġtarget Ġlanguage Ġ( i . e ., ĠEnglish , ĠChinese ) Ġto Ġsource Ġlanguage Ġ( i . e ., ĠGerman ) Ġwould Ġbe Ġsufficient Ġto Ġemploy Ġalready - assembled Ġdataset Ġand Ġpre - trained Ġunits . </s> Future ĠWork Ġ: :: ĠA cknowled gments . </s> We Ġgrate fully Ġacknowledge Ġthe ĠO MB + Ġteam Ġfor Ġthe Ġcollaboration Ġand Ġespecially Ġthank ĠR ued i ĠSe iler Ġand ĠMichael ĠHe im ann Ġfor Ġtheir Ġhelpful Ġfeedback Ġand Ġtechnical Ġsupport . ĠWe Ġare Ġindebted Ġto Ġthe Ġtut ors Ġfor Ġthe Ġevaluation Ġof Ġthe Ġsystem , Ġas Ġwell Ġas Ġto Ġthe Ġanonymous Ġreviewers Ġfor Ġtheir Ġvaluable Ġcomments . </s> OM B + ĠDesign </s> Figure ĠFIG REF 52 Ġpresents Ġan Ġexample Ġof Ġthe ĠO MB + ĠOnline ĠLearning ĠPlatform . </s> Mut ually ĠExclusive ĠRules </s> Ass ume Ġa Ġlist Ġof Ġall Ġtheoretically Ġpossible Ġdialogue Ġstates : Ġ$ S $ Ġ= Ġ[ topic , Ġsub - topic , Ġtraining , Ġexercise , Ġchapter Ġlevel , Ġsection Ġlevel , Ġquiz , Ġfinal Ġexamination , Ġquestion Ġnumber ] Ġand Ġfor Ġeach Ġelement Ġ$ s _{ n } $ Ġin Ġ$ S $ Ġis Ġtrue Ġthat : </s> This Ġwould Ġgive Ġus Ġall Ġgeneral Ġ( resp . Ġpossible ) Ġdialogue Ġstates Ġwithout Ġreference Ġto Ġthe Ġdesign Ġof Ġthe ĠO MB + Ġplatform . ĠHowever , Ġto Ġmake Ġthe Ġdialogue Ġstates Ġfully Ġsuitable Ġfor Ġthe ĠO MB +, Ġfrom Ġthe Ġgeneral Ġstates , Ġwe Ġtake Ġonly Ġthose , Ġwhich Ġare Ġvalid . ĠTo Ġdefine Ġthe Ġvalid ness Ġof Ġthe Ġstate , Ġwe Ġspecify Ġthe Ġfollowing Ġfive ĠMut ually ĠExclusive ĠRules Ġ( MER ): </s> Rule Ġ($ R _{ 1 } $ ) Ġin ĠTable ĠT AB REF 54 Ġdenotes Ġad missible Ġconfigurations Ġfor Ġtopic Ġand Ġmeans Ġthat Ġwe Ġare Ġeither Ġgiven Ġa Ġtopic Ġ($ T $ ) Ġor Ġnot Ġ($ \ l not ĠT $ ). </s> Rule Ġ($ R _{ 2 } $ ) Ġin ĠTable ĠT AB REF 55 Ġdenotes Ġthat Ġeither Ġno Ġinformation Ġon Ġthe Ġexamination Ġmode Ġis Ġgiven , Ġor Ġexamination Ġmode Ġis ĠTraining Ġ($ TR $ ) Ġor ĠExercise Ġ($ E $ ) Ġor ĠQu iz Ġ($ Q $ ) Ġor ĠFinal ĠExamination Ġ($ FE $ ), Ġbut Ġnot Ġmore Ġthan Ġone Ġmode Ġat Ġthe Ġsame Ġtime . </s> Rule Ġ($ R _{ 3 } $ ) Ġin ĠTable ĠT AB REF 56 Ġindicates Ġthat Ġeither Ġno Ġlevel Ġinformation Ġis Ġprovided , Ġor Ġthe Ġlevel Ġcorresponds Ġto Ġchapter Ġlevel Ġ($ CL $ ) Ġor Ġto Ġsection Ġlevel Ġ($ SL $ ), Ġbut Ġnot Ġto Ġboth Ġat Ġthe Ġsame Ġtime . </s> Rule Ġ($ R _{ 4 } $ ) Ġin ĠTable ĠT AB REF 57 Ġmeans Ġthat ĠTraining Ġ($ TR $ ) Ġand ĠExercise Ġ($ E $ ) Ġexamination Ġmodes Ġcan Ġeither Ġbelong Ġto Ġchapter Ġlevel Ġ($ CL $ ) Ġor Ġto Ġsection Ġlevel Ġ($ SL $ ), Ġbut Ġnot Ġto Ġboth Ġat Ġthe Ġsame Ġtime . </s> Rule Ġ($ R _{ 5 } $ ) Ġin ĠTable ĠT AB REF 58 Ġsymbol izes Ġthat Ġwe Ġcould Ġbe Ġeither Ġgiven Ġonly Ġa Ġtopic Ġ($ T $ ) Ġor Ġthe Ġcombination Ġof Ġtopic Ġand Ġsub - topic Ġ($ ST $ ) Ġat Ġthe Ġsame Ġtime , Ġor Ġonly Ġsub - topic , Ġor Ġno Ġinformation Ġon Ġthis Ġpoint Ġat Ġall . </s> We Ġthen Ġdefine Ġa Ġvalid Ġdialogue Ġstate , Ġas Ġa Ġdialogue Ġstate Ġthat Ġmeets Ġall Ġrequirements Ġof Ġthe Ġabove mentioned Ġrules : </s> After Ġwe Ġget Ġthe Ġvalid Ġstates Ġfor Ġour Ġdialog ues , Ġwe Ġwant Ġto Ġmake Ġa Ġmapping Ġfrom Ġeach Ġvalid Ġdialogue Ġstate Ġto Ġthe Ġnext Ġpossible Ġsystems Ġaction . ĠFor Ġthat , Ġwe Ġfirst Ġdefine Ġfive Ġtransition Ġrules Ġ: </s> me ans Ġthat Ġno Ġtopic Ġ($ T $ ) Ġis Ġfound Ġin Ġthe ĠID Ġ( i . e ., Ġcould Ġnot Ġbe Ġextracted Ġfrom Ġuser Ġinput ). </s> ind icates Ġthat Ġno Ġexamination Ġmode Ġ($ EM $ ) Ġis Ġfound Ġin Ġthe ĠID . </s> den otes Ġthat Ġthe Ġextracted Ġexamination Ġmode Ġ($ EM $ ) Ġis Ġeither ĠTraining Ġ($ TR $ ) Ġor ĠExercise Ġ($ E $ ). </s> me ans Ġthat Ġno Ġsub - topic Ġ($ ST $ ) Ġis Ġprovided Ġby Ġa Ġuser , Ġbut ĠID Ġeither Ġalready Ġcontains Ġthe Ġcombination Ġof Ġtopic Ġ($ T $ ), Ġtraining Ġ($ TR $ ) Ġand Ġsection Ġlevel Ġ($ SL $ ), Ġor Ġthe Ġcombination Ġof Ġtopic , Ġexercise Ġ($ E $ ) Ġand Ġsection Ġlevel , Ġor Ġthe Ġcombination Ġof Ġtopic Ġand Ġquiz Ġ($ Q $ ). </s> ind icates Ġthat Ġno Ġquestion Ġnumber Ġ($ Q NR $ ) Ġwas Ġprovided Ġby Ġa Ġstudent Ġ( or Ġcould Ġnot Ġbe Ġsuccessfully Ġextracted ). </s> Finally , Ġwe Ġassumed Ġthe Ġlist Ġof Ġpossible Ġnext Ġactions Ġfor Ġthe Ġsystem : </s> Following Ġthe Ġtransition Ġrules , Ġwe Ġmapped Ġeach Ġvalid Ġdialogue Ġstate Ġto Ġthe Ġpossible Ġnext Ġaction Ġ$ a _{ m } $ Ġin Ġ$ A $ : </s> in Ġthe Ġcase Ġwhere Ġwe Ġdo Ġnot Ġhave Ġany Ġtopic Ġprovided , Ġthe Ġnext Ġaction Ġis Ġto Ġask Ġfor Ġthe Ġtopic Ġ($ T $ ). </s> if Ġno Ġexamination Ġmode Ġis Ġprovided Ġby Ġa Ġuser Ġ( or Ġit Ġcould Ġnot Ġbe Ġsuccessfully Ġextracted Ġfrom Ġthe Ġuser Ġquery ), Ġthe Ġnext Ġaction Ġis Ġdefined Ġas Ġask Ġfor Ġexamination Ġmode Ġ($ EM $ ). </s> in Ġcase Ġwhere Ġwe Ġknow Ġthe Ġexamination Ġmode Ġ$ EM $ Ġ$\ in Ġ$ Ġ[ Training , ĠExercise ], Ġwe Ġhave Ġto Ġask Ġabout Ġthe Ġlevel Ġ( i . e ., Ġtraining Ġat Ġchapter Ġlevel Ġor Ġtraining Ġat Ġsection Ġlevel ), Ġthus Ġthe Ġnext Ġaction Ġis Ġask Ġfor Ġlevel Ġ($ L $ ). </s> if Ġno Ġsub - topic Ġis Ġprovided , Ġbut Ġthe Ġexamination Ġmode Ġ$ EM $ Ġ$\ in Ġ$ Ġ[ Training , ĠExercise ] Ġat Ġsection Ġlevel , Ġthe Ġnext Ġaction Ġis Ġdefined Ġas Ġask Ġfor Ġsub - topic Ġ($ ST $ ). </s> if Ġno Ġquestion Ġnumber Ġis Ġprovided Ġby Ġa Ġuser , Ġthen Ġthe Ġnext Ġaction Ġis Ġask Ġfor Ġquestion Ġnumber Ġ($ Q NR $ ). </s> Com ple teness Ġof ĠID : ĠExample ĠCases </s> Below Ġare Ġexamples Ġof Ġtwo Ġfinal Ġcases Ġ( out Ġof Ġsix ), Ġwhere ĠID Ġis Ġconsidered Ġto Ġbe Ġcomplete : </s> Inter action ĠShow cases </s> Below Ġare Ġfive Ġsample Ġdialog ues Ġwith Ġvariable Ġflows .
Introduction Robotic Process Automation (RPA) is a type of software bots that simulates hand-operated human activities like entering data into a system, registering into accounts, and accomplishing straightforward but repetitive workflows BIBREF0. However, one of the drawbacks of RPA-bots is their susceptibility to changes in defined scenarios: being designed for a particular task, the RPA-bot is usually not adaptable to other domains or even light modifications in a workflow BIBREF0. This inability to readjust to shifting conditions gave rise to Intelligent Process Automation (IPA) systems. IPA-bots combine RPA with Artificial Intelligence (AI) and thus are able to execute more cognitively demanding tasks that require i.a. reasoning and language understanding. Hence, IPA-bots advanced beyond automating shallow “click tasks” and can perform jobs more intelligently – by means of machine learning algorithms. Such IPA-systems undertake time-consuming and routine tasks, and thus enable smart workflows and free up skilled workers to accomplish higher-value activities. One of the potential applications of Natural Language Processing (NLP) within the IPA domain are conversational interfaces that enable human-to-machine interaction. The main benefit of conversational systems is their ability to give attention to several users simultaneously while supporting natural communication. A conventional dialogue system comprises multiple stages and involves different types of NLP subtasks, starting with Natural Language Understanding (NLU) (e.g., intent classification, named entity extraction) and going towards dialogue management (i.e., determining the next possible bot action, considering the dialogue history) and response generation (e.g., converting the semantic representation of the next system action into a natural language utterance). A typical dialogue system for IPA purposes undertakes shallow customer support requests (e.g., answering of FAQs), allowing human workers to focus on more sophisticated inquiries. Recent research in the dialogue generation domain is conducted by employing AI-techniques like machine and deep learning BIBREF1, BIBREF2. However, conventional supervised methods have limitations when applied to real-world data and industrial tasks. The primary challenge here refers to a training phase since a robust model requires an extensive amount of structured and labeled data, that is often not available for domain-specific problems. Especially if it concerns dialogue data, which has to be appropriately structured as well as labeled and annotated with additional information. Therefore, despite the popularity of deep learning end-to-end models, one still needs to rely on conventional pipelines in practical dialogue engineering, especially while setting a new domain. However, with few structured data available, transfer learning methods can be used. Such algorithms enable training of the systems with less or even a minimal amount of data, and are able to transfer the knowledge obtained during the training on existing data to the unseen domain. Introduction ::: Outline and Contributions This paper addresses the challenge of implementing a dialogue system for IPA purposes within the practical e-learning domain with the initial absence of training data. Our contributions within this work are as follows: We implemented a robust dialogue system for IPA purposes within the practical e-learning domain and within the conditions of missing training (dialogue) data (see Section SECREF3 – Section SECREF4). The system is currently deployed at the e-learning platform. The system has two purposes: First, it reduces repetitive and time-consuming activities and, therefore, allows workers of the e-learning platform to focus solely on complex questions; Second, by interacting with users, it augments the resources with structured and to some extent labeled training data for further possible implementation of learnable dialogue components (see Section SECREF5); We showcased that even a small amount of structured dialogues could be successfully used for re-training of dialogue units by means of Transfer Learning techniques (see Section SECREF6). Target Domain & Task Definition OMB+ is a German e-learning platform that assists students who are preparing for an engineering or computer science study at a university. The central purpose of the course is to support students in reviving their mathematical skills so that they can follow the upcoming university courses. The platform is thematically segmented into 13 sections and includes free mathematical classes with theoretical and practical content. Besides that, OMB+ provides a possibility to get assistance from a human tutor via a chat interface. Usually, the students and tutors interact in written form, and the language of communication is German. The current problem of the OMB+ platform is that the number of students grows every year, but to hire more qualified human tutors is challenging and expensive. This results in a more extended waiting period for students until their problems can be considered. In general, student questions can be grouped into three main categories: organizational questions (e.g., course certificate), contextual questions (e.g., content, theorem) and mathematical questions (e.g., exercises, solutions). To assist a student with a mathematical question, a tutor has to know the following regular information: What kind of topic (or sub-topic) a student has a problem with. At which examination mode (i.e., quiz, chapter level training or exercise, section level training or exercise, or final examination) the student is working right now. And finally, the exact question number and exact problem formulation. This means that a tutor has to request the same information every time a new dialogue opens, which is very time consuming and could be successfully solved by means of an IPA dialogue bot. Model The main objective of the proposed system is to interact with students at the beginning of every conversation and gather information on the topic (and sub-topic), examination mode and level, question number and exact problem formulation. Therefore, the system saves time for tutors and allows them to handle solely complex mathematical questions. Besides that, the system is implemented in a way such that it accumulates labeled dialogues in the background and stores them in a structured form. Model ::: Dialogue Modules Figure FIGREF82 (see the Appendix) displays the entire dialogue flow. In a nutshell, the system receives a user input, analyzes it and extracts information, if provided. If some of the required information is missing, the system asks the student to provide it. When all the information is collected, it will be automatically validated and subsequently forwarded to a human tutor, who then can directly proceed with the assistance. In the following we will describe the central components of the system. OMB+ Design: Figure FIGREF52 (see Section SECREF11 of the Appendix) illustrates the internal structure and design of the OMB+ platform. It has topics and sub-topics, as well as four examination modes. Each topic (Figure FIGREF52, tag 1) corresponds to a chapter level and always has sub-topics (Figure FIGREF52, tag 2), which correspond to a section level. Examination modes training and exercise are ambiguous, because they correspond to either a chapter (Figure FIGREF52, tag 3) or a section (Figure FIGREF52, tag 5) level, and it is important to differentiate between them, since they contain different types of content. The mode final examination (Figure FIGREF52, tag 4) always corresponds to a chapter level, whereas quiz (Figure FIGREF52, tag 5) can belong only to a section level. According to the design of the OMB+ platform, there are several ways of how a possible dialogue flow can proceed. Preprocessing: In a natural language dialogue, a user may respond in many different ways, thus, the extraction of any data from user-generated text is a challenging task due to a number of misspellings or confusable spellings (e.g., Exercise 1.a, Exercise 1 (a)). Therefore, to enable a reliable extraction of entities, we preprocessed and normalized (e.g., misspellings, synonyms) every user input before it was sent to the Natural Language Understanding (NLU) module. The preprocessing includes following steps: lowercasing and stemming of all words in the input; removal of German stop words and punctuation; all mentions of $x$ in mathematical formulas were removed to avoid confusion with roman number 10 (“$X$”); in a combination of the type: word “Chapter/Exercise” + digit written as a word (i.e “first”, “second”), word was replaced with a digit (“in first Chapter” $\rightarrow $ “in Chapter 1”), roman numbers were replaced with digits as well (“Chapter IV” $\rightarrow $ “Chapter 4”). detected ambiguities were normalized (e.g., “Trainingsaufgabe” $\rightarrow $ “Training”); recognized misspellings resp. typos were corrected (e.g., “Difeernzialrechnung” $\rightarrow $ “Differentialrechnung')' permalinks were parsed and analyzed. From each permalink it is possible to extract topic, examination mode and question number; Natural Language Understanding (NLU): We implemented an NLU unit utilizing handcrafted rules, Regular Expressions (RegEx) and Elasticsearch (ES) API. The NLU module contains following functionalities: Intent classification: As we mentioned above, student questions can be grouped into three main categories: Organizational questions, contextual questions and mathematical questions. To classify the input message by its category or so-called intent, we utilized key-word information predefined by handcrafted rules. We assumed that particular words are explicit and associated with a corresponding intent. If no intent could be classified, then it is assumed that the NLU unit was not capable of understanding and the intent is interpreted as unknown. In this case, the system requests the user to provide an intent manually (by picking one from the mentioned three options). The questions from organizational and theoretical categories are directly delivered to a human tutor, while mathematical questions are processed by the automated system for further analysis. Entity Extraction: Next, the system attempts to retrieve the entities from a user message on the topic (and sub-topic), examination mode and level, and question number. This part is implemented using Elasticsearch (ES) and RegEx. To enable the use of ES, we indexed the OMB+ site to an internal database. Besides indexing the topics and titles, we also provided information on possible synonyms or writing styles. We additionally filed OMB+ permalinks, which direct to the site pages. To query the resulting database, we utilized the internal Elasticsearch multi match function and set the minimum should match parameter to $20\%$. This parameter defines the number of terms that must match for a document to be considered relevant. Besides that, we adopted fuzziness with the maximum edit distance set to 2 characters. The fuzzy query uses similarity based on Levenshtein edit distance BIBREF3. Finally, the system generates a ranked list of possible matching entries found in the database within the predefined relevance threshold (we set it to $\theta $=$1.5$). We pick the most probable entry as the correct one and extract the corresponding entity from the user input. To summarize, the NLU module receives the user input as a preprocessed text and checks it across all predefined RegEx statements and for a match in the Elasticsearch database. Every time the entity is extracted, it is entered in the Information Dictionary (ID). The ID has the following six slots to be filled in: topic, sub-topic, examination level, examination mode, question number, and exact problem formulation. Dialogue Manager consists of the Dialogue State Tracker (DST), that maintains a representation of the current dialog state, and of the Policy Learner (PL) that defines the next system action. In our model, the system's next action is defined by the state of the previously obtained information stored in the Information Dictionary. For instance, if the system recognizes that the student works on the final examination, it also understands (defined by the logic in the predefined rules) that there is no need to ask for sub-topic because the final examination always corresponds to a chapter level (due to the design of OMB+ platform). If the system identifies that the user has difficulties in solving a quiz, it has to ask for the corresponding topic and sub-topic if not yet provided by a user (because the quiz always refers to a section level). To determine all of the potential dialogue flows, we implemented Mutually Exclusive Rules (MER), which indicate that two events $e_{1}$ and $e_{2}$ are mutually exclusive or disjoint if they cannot both occur at the same time (thus, the intersection of these events is empty: $P(A \cap B) = 0$). Additionally, we defined transition and mapping rules. The formal explanation of rules can be found in Section SECREF12 of the Appendix. Following the rules, we generated 56 state transitions, which define next system actions. Being on a new dialogue state, the system compares the extracted (i.e., updated) information in the ID with the valid dialogue states (see Section SECREF12 of the Appendix for the explanation of the validness) and picks the mapped action as the next system's action. The abovementioned rules are intended to support the current design of the OMB+ learning platform. However, additional MERs could be added to generate new transitions. Exemplifying this, we conducted experiments with the former design of the platform, where all the topics, except for the first one, had only sub-topics, whereas the first topic had both sub-topics and sub-sub-topics. We could effortlessly generate the missing transitions with our approach. The number of possible transitions, in this case, increased from 56 to 117. Meta Policy: Since the system is intended to operate in a real-world scenario, we had to implement additional policies that control the dialogue flow and validate the system's accuracy. Below we describe these policies: Completeness of the Information Dictionary: In this step, the system validates the completeness of the ID, which is defined by the number of obligatory slots filled in the information dictionary. There are 6 distinct cases when the ID is considered to be complete (see Section SECREF13 of the Appendix). For instance, if a user works on a final examination, the system does not has to request a sub-topic or examination level. Thus, the ID has to be filled only with data for a topic, examination mode, and question number, whereas, if the user works on a quiz, the system has to gather information about the topic, sub-topic, examination mode, and the question number. Once the ID is complete, it is provided to the verification step. Otherwise, the system proceeds according to the next action. The system extracts the information in each dialogue step, and thus if the user provides updated information on any subject later in the dialogue, the corresponding slot will be updated in the ID. Verification Step: Once the system has obtained all the necessary information (i.e., ID is complete), it proceeds to the final verification. In that step, the collected data is shown to the student in the session. The student is asked to verify the correctness of the collected data and if some entries are wrong, to correct them. The ID is, where necessary, updated with the user-provided data. This procedure repeats until the user confirms the correctness of the assembled data. Fallback Policy: In some cases, the system fails to derive information from a student query, even if the student provides it. It is due to the Elasticsearch functionality and previously unseen RegEx patterns. In these cases, the system re-asks a user and attempts to retrieve information from a follow-up query. The maximum number of re-ask attempts is set to three times ($r=3$). If the system is unable to extract information after three times, the user input is considered as the ground truth and saved to the appropriate slot in ID. An exception to this rule applies where the user has to specify the intent manually. In this case, after three unclassified attempts, a session is directly handed over to a human tutor. Human Request: In each dialogue state, a user can switch to a human tutor. For this, a user can enter the human key-word. Hence, every user message is additionally analyzed for the presence of this key-word. Response Generation: In this module, the semantic representation of the system's next action is transformed into natural language. Hence, each possible action is mapped to precisely one utterance, which is stored in the templates. Some of the predefined responses are fixed (i.e., “Welches Kapitel bearbeitest du gerade?”), others have placeholders for system values. In the latter case, the utterance can be formulated dependent on the actual ID. The dialogue showcases can be found in Section SECREF14 of the Appendix. Evaluation In order to get the feedback on the quality, functionality, and usefulness of the introduced model, we evaluated it in two ways: first, with an automated method using 130 manually annotated dialogues, to prove the robustness of the system, and second – with human tutors from OMB+ – to investigate the user experience. We describe the details as well as the most common errors below. Evaluation ::: Automated Evaluation To conduct an automated evaluation, we manually created a dataset with 130 dialog scenarios. We took real first user questions and predefined possible user responses, as well as gold system answers. These scenarios cover most frequent dialogues that we previously saw in human-to-human dialogue data. We tested our system by implementing a self-chatting evaluation bot. The evaluation cycle could be described as follows: The system receives the first question from a predefined dialogue via an API request, preprocesses and analyzes it in order to extract entities. Then it estimates the applicable next action, and responds according to it. This response is then compared to the system's gold answer: If the predicted answer is correct, then the system receives the next predefined user input from the dialog and responds again, as defined above. This procedure continues until the dialog terminates (i.e., ID is complete). Otherwise, the system fails, reporting the unsuccessful case number. Our final system successfully passed this evaluation for all 130 cases. Evaluation ::: Human Evaluation and Error Analysis To evaluate our system on irregular examples, we conducted experiments with human tutors from the OMB+ platform. The tutors are experienced regarding rare or complex questions, ambiguous answers, misspellings and other infrequent but still very relevant problems, which occur during a dialogue. In the following, we investigate some common errors and make additional observations. Misspellings and confusable spellings occur quite often in the user-generated text, and since we attempt to let the conversation remain very natural from the user side and thus, cannot require formal writing, we have to deal with various writing issues. One of the most frequent problems is misspellings. German words are generally long and can be complicated, and since users type quickly, this often leads to the wrong order of characters within words. To tackle this challenge, we used fuzzy match within ES. However, the maximum allowed edit distance in Elasticsearch is set to 2 characters. This means, that all the misspellings beyond this threshold could not be accurately recognized by ES (e.g., Differentialrechnung vs Differnezialrechnung). Another characteristic example would be the writing of the section or question number. The equivalent information can be written in several distinct ways, which has to be considered in our RegEx unit (e.g., Exercise 5 a, Exercise V a, Exercise 5 (a)). A similar problem occurs with confusable spelling (i.e.: Differentialrechnung vs Differentialgleichung). We analyzed the cases mentioned above and added some of the most common issues to the ES database or handled them with RegEx during the preprocessing step. Elasticsearch Threshold: In some cases, the system failed to extract information, although the user provided it. In other cases, ES extracts information that was not mentioned in a user query at all. That occurs due to the relevancy scoring algorithm of Elasticsearch, where a document's score is a combination of textual similarity and other metadata based scores. Our analysis revealed that ES mostly fails to extract the information if the sentence (i.e., user message) is quite short (e.g., 5 words). To overcome this difficulty, we combined the current input $u_{t}$ with the dialog history. This step eliminated the problem and improved the retrieval quality. To solve the case where Elasticsearch extracts incorrect information (or information that was not mentioned in a query) is more challenging. We discovered that the problem comes from short words or sub-words (e.g., suffixes, prefixes), which ES considers to be credible enough. The Elasticsearch documentation suggests getting rid of stop words to eliminate this behavior. However, this did not improve the search in our case. Also, fine-tuning of ES parameters such as the relevance threshold, prefix length and minimum should match parameter did not bring significant improvements. To cope with this problem, we implemented a verification step, where a user is given a chance to correct the erroneously retrieved information. The overall feedback from the tutors included reduced repetitive activities as well as reduced waiting times for students until their questions were processed. Also, tutors reported that the rate of cancelled sessions (switching to a human tutor) is rather low. Structured Dialogue Acquisition As we already mentioned, our system attempts to support the human tutor by assisting students, but it also collects structured and labeled training data in the background. In a trial run of the rule-based system, we were able to accumulate a toy-dataset with training dialogues. The assembled dialogues have the following format: Plain dialogues with unique dialogue indexes; Plain Information Dictionary information (e.g., extracted entities) collected for the whole dialogue; Pairs of questions (i.e., user requests) and responses (i.e., bot responses) with the unique dialogue- and turn-indexes; Triples in the form of (User Request, Next Action, Response). Information on the next system's action could be employed to train a Dialogue Manager unit with (deep-) machine learning algorithms; For each state in the dialogue, we saved the entities that the system was able to extract from the provided user query, along with their position in the utterance. This information could be used to train a custom, domain specific Named Entity Recognition model. Re-implementation of units with BERT As we mentioned before, there are many cases, especially in the industry, where the labeled and structured data is not directly available. Collecting and labeling such data is often a tedious and time-consuming task. Thus, algorithms that enable training of the systems with less or even a minimal amount of data are highly required. Such algorithms can transfer the knowledge obtained during the training on existing data to the unseen domain. They are, therefore, one of the potential solutions for industrial problems. Once we assembled a dataset of structured data via our rule-based system, we re-implemented two out of three central dialogue components in our conversational assistant with deep learning methods. Since the available data was collected in a trial-run and thus the obtained dataset was rather small to train a machine learning model from scratch, we utilized the Transfer Learning approach, and fine-tuned the existing pre-trained model (i.e., BERT) for our target domain and data. For the experiments, we defined two tasks: First, we studied the Named Entity Recognition problem in a custom domain setting. We defined a sequence labeling task and employed the BERT model BIBREF4. We applied the model to our dataset and fine-tuned it for six (6) domain-specific (i.e., e-learning) entities and one (1) “unknown” label. Second, we investigated the effectiveness of BERT for the dialogue manager core. For that experiment, we defined a classification task and applied the model to predict the system's Next Action for every given user utterance in a conversation. We then computed the macro F-score for 13 possible actions and an average dialogue accuracy. Finally, we verified that the applied model performed well on both tasks: We achieved the performance of $0.93$ macro F1 points for Named Entity Recognition (NER) and $0.75$ macro F1 points for the Next Action Prediction (NAP) task. We, therefore, conclude that both NER and NAP components could be employed to substitute or extend the existing rule-based modules. Data & Descriptive Statistics: The dataset that we collected during the trial-run consists of 300 structured dialogues with the average length of a dialogue being six (6) utterances. Communication with students was performed in the German language. Detailed general statistics can be found in Table TABREF41. Named Entity Recognition: We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance. Since the data obtained from the rule-based system already contains information on the entities extracted from each user query (i.e., by means of Elasticsearch), we could use it to train a domain-specific NER unit. However, since the user-input was informal, the same information could be provided in different writing styles. That means that a single entity could have different surface forms (e.g., synonyms, writing styles) (although entities that we extracted from the rule-based system were all converted to a universal standard, e.g., official chapter names). To consider all of the variable entity forms while post-labeling the original dataset, we defined generic entity names (e.g., chapter, question nr.) and mapped variations of entities from the user input (e.g., Chapter = [Elementary Calculus, Chapter $I$, ...]) to them. Next Action Prediction: We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required. We investigated two settings: Default Setting: Using only a user input and the corresponding label (i.e., next action) without additional context. By default, we run all of our experiments in this setting. Extended Setting: Using a user input, a corresponding next action, and a previous system action as a source of additional context. For this setting, we run an experiment with the best performing model from the default setting. The overall dataset consists of 300 labeled dialogues, where 200 (with 1161 utterances) of them were employed for training, and 100 for evaluation and test sets (50 dialogues with about 300 utterances for each set respectively). Model Settings: For the NER task we conducted experiments with German and multilingual BERT implementations. Since in the German language the capitalization of words plays a significant role, we run our tests on the capitalized input, while keeping the original punctuation. Hence, we employed the available base model for both multilingual and German BERT implementations in the cased version. We set the learning rate for both models to $1e-4$ and the maximum length of the tokenized input was set to 128 tokens. We run the experiments multiple times with different seeds for a maximum of 50 epochs with the training batch size set to 32. We utilized AdamW as the optimizer and employed early stopping, if the performance did not change significantly after 5 epochs. For the NAP task we conducted experiments with German and multilingual BERT implementations as well. Here, we investigated the performance of both capitalized and lowercased input, as well as plain and preprocessed data. For the multilingual BERT, we employed the base model in both cased and uncased variations. For the German BERT, we utilized the base model in the cased variation only. For both models, we set the learning rate to $4e-5$, and the maximum length of the tokenized input was set to 128 tokens. We run the experiments multiple times with different seeds for a maximum of 300 epochs with the training batch size set to 32. We utilized AdamW as the optimizer and employed early stopping, if the performance did not change significantly after 15 epochs. Evaluation and Discussion: For the evaluation, we computed word-level macro F1 score for the NER task and utterance-level macro F1 score for the NAP task. The word-level F1 is estimated as the average of the F1 scores per class, each computed from all words in the evaluation and test sets. The results for the NER task are depicted in Table TABREF49. For utterance-level F1, a single label (i.e., next action) is obtained for the whole utterance. The results for the NAP task are presented in Table TABREF48. We additionally computed average dialogue accuracy for the best performing NAP models. This score denotes how well the predicted next actions match the gold next actions and thus form the dialogue flow within each conversation. The average dialogue accuracy was computed for 50 dialogues in the evaluation and test sets respectively. The results are displayed in Table TABREF50. The obtained results for the NER task revealed that German BERT performed significantly better than the multilingual BERT model. The performance of the custom NER unit is at $0.93$ macro F1 points for all possible named entities (see Table TABREF49). In contrast, for the NAP task, the multilingual BERT model obtained better performance than the German BERT model. Here, the best performing system in the default setting achieved a macro F1 of $0.677$ points for 14 possible labels, whereas the model in the extended setting performed better – its highest macro F1 score is $0.752$ for the same amount of labels (see Table TABREF48). Considering the dialogue accuracy, the extended system trained with multilingual BERT achieved better results than the default one with $0.801$ accuracy points compared to $0.724$ accuracy points for the test set (see Table TABREF50). The overall observation for the NAP is that the capitalized setting improved the performance of the model, whereas the inclusion of punctuation has not positively influenced the results. Error Analysis: After the evaluation step, we analyzed the cases, where the model failed to predict the correct action or labeled the named entity span erroneously. Below we describe the most common errors for both tasks. Next Action Prediction: One of the most frequent errors in the default model was the mismatch between two consecutive actions – namely, the action Question Number and Subtopic. That is due to the order of these actions in the conversational flow: Occurrence of both actions in the dialogue is not strict and substantially depends on the previous system action. However, the analysis of the extended model revealed that the introduction of additional context in the form of the previous action improved the performance of the system in this particular case by about $60\%$. Named Entity Recognition: The failing cases include mismatches between the tags “chapter” and “other”, and the tags “question number” and “other”. This type of error arose due to the imperfectly labeled span of a multi-word named entity. In such cases, the first or last word in the named entity was excluded from the span and erroneously labeled with the tag “other”. Related Work Individual components of a particular dialogue system could be implemented using a different kind of approach, starting with entirely rule- and template-based methods, and going towards hybrid approaches (using learnable components along with handcrafted units) and end-to-end trainable machine learning methods. Rule-based Approaches: Though many of the latest research approaches handle NLU and NLG units by using statistical NLP models BIBREF5, BIBREF6, BIBREF7, most of the industrially deployed dialogue systems still use manual features or handcrafted rules for the state and action prediction, intent classification, and slot filling tasks BIBREF8, BIBREF9. The rule-based approach ensures robustness and stable performance that is crucial for industrial systems that interact with a large number of users simultaneously. However, it is highly expensive and time-consuming to deploy a real dialogue system built in this manner. The major disadvantage is that the usage of handcrafted systems is restricted to a specific domain, and possible domain adaptation requires extensive manual engineering. End-to-End Learning Approaches: Due to the recent advance of end-to-end neural generative models BIBREF10, many efforts have been made to build an end-to-end trainable architecture for dialogue systems. Rather than using the traditional pipeline, an end-to-end model is conceived as a single module BIBREF8. Despite having better adaptability compared to any rule-based system and being easy to train, end-to-end approaches remain unattainable for commercial conversational agents operating on real-world data. A well and carefully constructed task-oriented dialogue system in a known domain using handcrafted rules and predefined responses, still outperforms the end-to-end systems due to its robustness BIBREF11, BIBREF12. Hybrid Approaches: Though end-to-end learning is an attractive solution for dialogue systems, current techniques are data-intensive and require large amounts of dialogues to learn simple actions. To overcome this difficulty, BIBREF13 (BIBREF13) introduce Hybrid Code Networks (HCNs), which is an ensemble of retrieval and trainable units. The authors report, that compared to existing end-to-end methods, their approach considerably reduces the amount of data required for training BIBREF13. Hybrid models appear to replace the established rule- and template-based approaches which are currently utilized in an industrial setting. Conclusions In this work, we implemented a dialogue system for Intelligent Process Automation purposes that simultaneously solves two problems: First, it reduces repetitive and time-consuming activities and, therefore, allows workers of the e-learning platform to focus on solely mathematical and hence more cognitively demanding questions. Second, by interacting with users, it augments the resources with structured and labeled training data for further possible implementation of learnable dialogue components. The realization of such a system was connected with many challenges. Among others were missing structured data, ambiguous or erroneous user-generated text and the necessity to deal with already existing corporate tools and their design. The introduced model allowed us to accumulate structured and to some extent labeled data without any special efforts from the human (i.e., tutors) side (e.g., manual annotation of existing dialogues, change of the conversational structure). Once we collected structured dialogues, we were able to re-train specific components of the system with deep learning methods and achieved reasonable performance for all proposed tasks. We believe the obtained results are rather good, considering a relatively small amount of data we utilized to fine-tune the pre-trained model. We, therefore, conclude that both Next Action Prediction and Named Entity Recognition components could be employed to substitute or extend the existing rule-based modules. Rule-based units are restricted in their capabilities and could be hardly adaptable to novel patterns, whereas the trainable units generalize better, which we believe could reduce the number of erroneous predictions in case of unexpected dialogue behavior. Furthermore, to increase the overall robustness, both rule-based and trainable components could be used synchronously as a hybrid model: in case when one system fails, the dialogue proceeds on the prediction obtained from the other model. Future Work The core of the rule-based model is a dialogue manager that determines the current state of the conversation and the possible next action. Rule-based systems are generally considered to be hardly adaptable to new domains; however, our dialogue manager proved to be flexible to slight modifications in a workflow. One of the possible directions of future work would be the investigation of the general adaptability of the dialogue manager core to other scenarios and domains (e.g., different course). Further investigation could be towards the multi-language modality for the re-implemented units. Since the OMB+ platform also supports English and Chinese, it would be interesting to examine whether the simple translation from target language (i.e., English, Chinese) to source language (i.e., German) would be sufficient to employ already-assembled dataset and pre-trained units. Future Work ::: Acknowledgments. We gratefully acknowledge the OMB+ team for the collaboration and especially thank Ruedi Seiler and Michael Heimann for their helpful feedback and technical support. We are indebted to the tutors for the evaluation of the system, as well as to the anonymous reviewers for their valuable comments. OMB+ Design Figure FIGREF52 presents an example of the OMB+ Online Learning Platform. Mutually Exclusive Rules Assume a list of all theoretically possible dialogue states: $S$ = [topic, sub-topic, training, exercise, chapter level, section level, quiz, final examination, question number] and for each element $s_{n}$ in $S$ is true that: This would give us all general (resp. possible) dialogue states without reference to the design of the OMB+ platform. However, to make the dialogue states fully suitable for the OMB+, from the general states, we take only those, which are valid. To define the validness of the state, we specify the following five Mutually Exclusive Rules (MER): Rule ($R_{1}$) in Table TABREF54 denotes admissible configurations for topic and means that we are either given a topic ($T$) or not ($\lnot T$). Rule ($R_{2}$) in Table TABREF55 denotes that either no information on the examination mode is given, or examination mode is Training ($TR$) or Exercise ($E$) or Quiz ($Q$) or Final Examination ($FE$), but not more than one mode at the same time. Rule ($R_{3}$) in Table TABREF56 indicates that either no level information is provided, or the level corresponds to chapter level ($CL$) or to section level ($SL$), but not to both at the same time. Rule ($R_{4}$) in Table TABREF57 means that Training ($TR$) and Exercise ($E$) examination modes can either belong to chapter level ($CL$) or to section level ($SL$), but not to both at the same time. Rule ($R_{5}$) in Table TABREF58 symbolizes that we could be either given only a topic ($T$) or the combination of topic and sub-topic ($ST$) at the same time, or only sub-topic, or no information on this point at all. We then define a valid dialogue state, as a dialogue state that meets all requirements of the abovementioned rules: After we get the valid states for our dialogues, we want to make a mapping from each valid dialogue state to the next possible systems action. For that, we first define five transition rules : means that no topic ($T$) is found in the ID (i.e., could not be extracted from user input). indicates that no examination mode ($EM$) is found in the ID. denotes that the extracted examination mode ($EM$) is either Training ($TR$) or Exercise ($E$). means that no sub-topic ($ST$) is provided by a user, but ID either already contains the combination of topic ($T$), training ($TR$) and section level ($SL$), or the combination of topic, exercise ($E$) and section level, or the combination of topic and quiz ($Q$). indicates that no question number ($QNR$) was provided by a student (or could not be successfully extracted). Finally, we assumed the list of possible next actions for the system: Following the transition rules, we mapped each valid dialogue state to the possible next action $a_{m}$ in $A$: in the case where we do not have any topic provided, the next action is to ask for the topic ($T$). if no examination mode is provided by a user (or it could not be successfully extracted from the user query), the next action is defined as ask for examination mode ($EM$). in case where we know the examination mode $EM$ $\in $ [Training, Exercise], we have to ask about the level (i.e., training at chapter level or training at section level), thus the next action is ask for level ($L$). if no sub-topic is provided, but the examination mode $EM$ $\in $ [Training, Exercise] at section level, the next action is defined as ask for sub-topic ($ST$). if no question number is provided by a user, then the next action is ask for question number ($QNR$). Completeness of ID: Example Cases Below are examples of two final cases (out of six), where ID is considered to be complete: Interaction Showcases Below are five sample dialogues with variable flows.
Do they use off-the-shelf NLP systems to build their assitant?
ee417fea65f9b1029455797671da0840c8c1abbe
Do Ġthey Ġuse Ġoff - the - she lf ĠN LP Ġsystems Ġto Ġbuild Ġtheir Ġass itant ?
No
<s> Do Ġthey Ġuse Ġoff - the - she lf ĠN LP Ġsystems Ġto Ġbuild Ġtheir Ġass itant ? </s> Introduction </s> Rob otic ĠProcess ĠAutom ation Ġ( R PA ) Ġis Ġa Ġtype Ġof Ġsoftware Ġbots Ġthat Ġsim ulates Ġhand - operated Ġhuman Ġactivities Ġlike Ġentering Ġdata Ġinto Ġa Ġsystem , Ġregistering Ġinto Ġaccounts , Ġand Ġaccompl ishing Ġstraightforward Ġbut Ġrepetitive Ġwork flows ĠB IB REF 0 . ĠHowever , Ġone Ġof Ġthe Ġdrawbacks Ġof ĠR PA - bots Ġis Ġtheir Ġsusceptibility Ġto Ġchanges Ġin Ġdefined Ġscenarios : Ġbeing Ġdesigned Ġfor Ġa Ġparticular Ġtask , Ġthe ĠR PA - bot Ġis Ġusually Ġnot Ġadapt able Ġto Ġother Ġdomains Ġor Ġeven Ġlight Ġmodifications Ġin Ġa Ġworkflow ĠB IB REF 0 . ĠThis Ġinability Ġto Ġread just Ġto Ġshifting Ġconditions Ġgave Ġrise Ġto ĠIntelligent ĠProcess ĠAutom ation Ġ( IP A ) Ġsystems . ĠIPA - bots Ġcombine ĠR PA Ġwith ĠArtificial ĠIntelligence Ġ( AI ) Ġand Ġthus Ġare Ġable Ġto Ġexecute Ġmore Ġcognitive ly Ġdemanding Ġtasks Ġthat Ġrequire Ġi . a . Ġreasoning Ġand Ġlanguage Ġunderstanding . ĠHence , ĠIPA - bots Ġadvanced Ġbeyond Ġautom ating Ġshallow ĠâĢ ľ click Ġtasks âĢ Ŀ Ġand Ġcan Ġperform Ġjobs Ġmore Ġintellig ently ĠâĢĵ Ġby Ġmeans Ġof Ġmachine Ġlearning Ġalgorithms . ĠSuch ĠIPA - system s Ġundertake Ġtime - consuming Ġand Ġroutine Ġtasks , Ġand Ġthus Ġenable Ġsmart Ġwork flows Ġand Ġfree Ġup Ġskilled Ġworkers Ġto Ġaccomplish Ġhigher - value Ġactivities . </s> One Ġof Ġthe Ġpotential Ġapplications Ġof ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġwithin Ġthe ĠIPA Ġdomain Ġare Ġconvers ational Ġinterfaces Ġthat Ġenable Ġhuman - to - machine Ġinteraction . ĠThe Ġmain Ġbenefit Ġof Ġconvers ational Ġsystems Ġis Ġtheir Ġability Ġto Ġgive Ġattention Ġto Ġseveral Ġusers Ġsimultaneously Ġwhile Ġsupporting Ġnatural Ġcommunication . ĠA Ġconventional Ġdialogue Ġsystem Ġcomprises Ġmultiple Ġstages Ġand Ġinvolves Ġdifferent Ġtypes Ġof ĠN LP Ġsubt asks , Ġstarting Ġwith ĠNatural ĠLanguage ĠUnderstanding Ġ( NL U ) Ġ( e . g ., Ġintent Ġclassification , Ġnamed Ġentity Ġextraction ) Ġand Ġgoing Ġtowards Ġdialogue Ġmanagement Ġ( i . e ., Ġdetermining Ġthe Ġnext Ġpossible Ġbot Ġaction , Ġconsidering Ġthe Ġdialogue Ġhistory ) Ġand Ġresponse Ġgeneration Ġ( e . g ., Ġconverting Ġthe Ġsemantic Ġrepresentation Ġof Ġthe Ġnext Ġsystem Ġaction Ġinto Ġa Ġnatural Ġlanguage Ġutter ance ). ĠA Ġtypical Ġdialogue Ġsystem Ġfor ĠIPA Ġpurposes Ġundert akes Ġshallow Ġcustomer Ġsupport Ġrequests Ġ( e . g ., Ġanswering Ġof ĠFAQ s ), Ġallowing Ġhuman Ġworkers Ġto Ġfocus Ġon Ġmore Ġsophisticated Ġinquiries . </s> Recent Ġresearch Ġin Ġthe Ġdialogue Ġgeneration Ġdomain Ġis Ġconducted Ġby Ġemploying ĠAI - techn iques Ġlike Ġmachine Ġand Ġdeep Ġlearning ĠB IB REF 1 , ĠB IB REF 2 . ĠHowever , Ġconventional Ġsupervised Ġmethods Ġhave Ġlimitations Ġwhen Ġapplied Ġto Ġreal - world Ġdata Ġand Ġindustrial Ġtasks . ĠThe Ġprimary Ġchallenge Ġhere Ġrefers Ġto Ġa Ġtraining Ġphase Ġsince Ġa Ġrobust Ġmodel Ġrequires Ġan Ġextensive Ġamount Ġof Ġstructured Ġand Ġlabeled Ġdata , Ġthat Ġis Ġoften Ġnot Ġavailable Ġfor Ġdomain - specific Ġproblems . ĠEspecially Ġif Ġit Ġconcerns Ġdialogue Ġdata , Ġwhich Ġhas Ġto Ġbe Ġappropriately Ġstructured Ġas Ġwell Ġas Ġlabeled Ġand Ġannot ated Ġwith Ġadditional Ġinformation . ĠTherefore , Ġdespite Ġthe Ġpopularity Ġof Ġdeep Ġlearning Ġend - to - end Ġmodels , Ġone Ġstill Ġneeds Ġto Ġrely Ġon Ġconventional Ġpipelines Ġin Ġpractical Ġdialogue Ġengineering , Ġespecially Ġwhile Ġsetting Ġa Ġnew Ġdomain . ĠHowever , Ġwith Ġfew Ġstructured Ġdata Ġavailable , Ġtransfer Ġlearning Ġmethods Ġcan Ġbe Ġused . ĠSuch Ġalgorithms Ġenable Ġtraining Ġof Ġthe Ġsystems Ġwith Ġless Ġor Ġeven Ġa Ġminimal Ġamount Ġof Ġdata , Ġand Ġare Ġable Ġto Ġtransfer Ġthe Ġknowledge Ġobtained Ġduring Ġthe Ġtraining Ġon Ġexisting Ġdata Ġto Ġthe Ġunseen Ġdomain . </s> Introduction Ġ: :: ĠOut line Ġand ĠContributions </s> This Ġpaper Ġaddresses Ġthe Ġchallenge Ġof Ġimplementing Ġa Ġdialogue Ġsystem Ġfor ĠIPA Ġpurposes Ġwithin Ġthe Ġpractical Ġe - learning Ġdomain Ġwith Ġthe Ġinitial Ġabsence Ġof Ġtraining Ġdata . ĠOur Ġcontributions Ġwithin Ġthis Ġwork Ġare Ġas Ġfollows : </s> We Ġimplemented Ġa Ġrobust Ġdialogue Ġsystem Ġfor ĠIPA Ġpurposes Ġwithin Ġthe Ġpractical Ġe - learning Ġdomain Ġand Ġwithin Ġthe Ġconditions Ġof Ġmissing Ġtraining Ġ( dial ogue ) Ġdata Ġ( see ĠSection ĠSEC REF 3 ĠâĢĵ ĠSection ĠSEC REF 4 ). ĠThe Ġsystem Ġis Ġcurrently Ġdeployed Ġat Ġthe Ġe - learning Ġplatform . </s> The Ġsystem Ġhas Ġtwo Ġpurposes : </s> First , Ġit Ġreduces Ġrepetitive Ġand Ġtime - consuming Ġactivities Ġand , Ġtherefore , Ġallows Ġworkers Ġof Ġthe Ġe - learning Ġplatform Ġto Ġfocus Ġsolely Ġon Ġcomplex Ġquestions ; </s> Second , Ġby Ġinteracting Ġwith Ġusers , Ġit Ġaug ments Ġthe Ġresources Ġwith Ġstructured Ġand Ġto Ġsome Ġextent Ġlabeled Ġtraining Ġdata Ġfor Ġfurther Ġpossible Ġimplementation Ġof Ġlearn able Ġdialogue Ġcomponents Ġ( see ĠSection ĠSEC REF 5 ); </s> We Ġshowcased Ġthat Ġeven Ġa Ġsmall Ġamount Ġof Ġstructured Ġdialog ues Ġcould Ġbe Ġsuccessfully Ġused Ġfor Ġre - training Ġof Ġdialogue Ġunits Ġby Ġmeans Ġof ĠTransfer ĠLearning Ġtechniques Ġ( see ĠSection ĠSEC REF 6 ). </s> Target ĠDomain Ġ& ĠTask ĠDefinition </s> OM B + Ġis Ġa ĠGerman Ġe - learning Ġplatform Ġthat Ġassists Ġstudents Ġwho Ġare Ġpreparing Ġfor Ġan Ġengineering Ġor Ġcomputer Ġscience Ġstudy Ġat Ġa Ġuniversity . ĠThe Ġcentral Ġpurpose Ġof Ġthe Ġcourse Ġis Ġto Ġsupport Ġstudents Ġin Ġrev iving Ġtheir Ġmathematical Ġskills Ġso Ġthat Ġthey Ġcan Ġfollow Ġthe Ġupcoming Ġuniversity Ġcourses . ĠThe Ġplatform Ġis Ġthem atically Ġsegment ed Ġinto Ġ13 Ġsections Ġand Ġincludes Ġfree Ġmathematical Ġclasses Ġwith Ġtheoretical Ġand Ġpractical Ġcontent . ĠBesides Ġthat , ĠO MB + Ġprovides Ġa Ġpossibility Ġto Ġget Ġassistance Ġfrom Ġa Ġhuman Ġtutor Ġvia Ġa Ġchat Ġinterface . ĠUsually , Ġthe Ġstudents Ġand Ġtut ors Ġinteract Ġin Ġwritten Ġform , Ġand Ġthe Ġlanguage Ġof Ġcommunication Ġis ĠGerman . ĠThe Ġcurrent Ġproblem Ġof Ġthe ĠO MB + Ġplatform Ġis Ġthat Ġthe Ġnumber Ġof Ġstudents Ġgrows Ġevery Ġyear , Ġbut Ġto Ġhire Ġmore Ġqualified Ġhuman Ġtut ors Ġis Ġchallenging Ġand Ġexpensive . ĠThis Ġresults Ġin Ġa Ġmore Ġextended Ġwaiting Ġperiod Ġfor Ġstudents Ġuntil Ġtheir Ġproblems Ġcan Ġbe Ġconsidered . </s> In Ġgeneral , Ġstudent Ġquestions Ġcan Ġbe Ġgrouped Ġinto Ġthree Ġmain Ġcategories : Ġorganizational Ġquestions Ġ( e . g ., Ġcourse Ġcertificate ), Ġcontextual Ġquestions Ġ( e . g ., Ġcontent , Ġtheorem ) Ġand Ġmathematical Ġquestions Ġ( e . g ., Ġexercises , Ġsolutions ). ĠTo Ġassist Ġa Ġstudent Ġwith Ġa Ġmathematical Ġquestion , Ġa Ġtutor Ġhas Ġto Ġknow Ġthe Ġfollowing Ġregular Ġinformation : ĠWhat Ġkind Ġof Ġtopic Ġ( or Ġsub - topic ) Ġa Ġstudent Ġhas Ġa Ġproblem Ġwith . ĠAt Ġwhich Ġexamination Ġmode Ġ( i . e ., Ġquiz , Ġchapter Ġlevel Ġtraining Ġor Ġexercise , Ġsection Ġlevel Ġtraining Ġor Ġexercise , Ġor Ġfinal Ġexamination ) Ġthe Ġstudent Ġis Ġworking Ġright Ġnow . ĠAnd Ġfinally , Ġthe Ġexact Ġquestion Ġnumber Ġand Ġexact Ġproblem Ġformulation . ĠThis Ġmeans Ġthat Ġa Ġtutor Ġhas Ġto Ġrequest Ġthe Ġsame Ġinformation Ġevery Ġtime Ġa Ġnew Ġdialogue Ġopens , Ġwhich Ġis Ġvery Ġtime Ġconsuming Ġand Ġcould Ġbe Ġsuccessfully Ġsolved Ġby Ġmeans Ġof Ġan ĠIPA Ġdialogue Ġbot . </s> Model </s> The Ġmain Ġobjective Ġof Ġthe Ġproposed Ġsystem Ġis Ġto Ġinteract Ġwith Ġstudents Ġat Ġthe Ġbeginning Ġof Ġevery Ġconversation Ġand Ġgather Ġinformation Ġon Ġthe Ġtopic Ġ( and Ġsub - topic ), Ġexamination Ġmode Ġand Ġlevel , Ġquestion Ġnumber Ġand Ġexact Ġproblem Ġformulation . ĠTherefore , Ġthe Ġsystem Ġsaves Ġtime Ġfor Ġtut ors Ġand Ġallows Ġthem Ġto Ġhandle Ġsolely Ġcomplex Ġmathematical Ġquestions . ĠBesides Ġthat , Ġthe Ġsystem Ġis Ġimplemented Ġin Ġa Ġway Ġsuch Ġthat Ġit Ġaccum ulates Ġlabeled Ġdialog ues Ġin Ġthe Ġbackground Ġand Ġstores Ġthem Ġin Ġa Ġstructured Ġform . </s> Model Ġ: :: ĠDialogue ĠMod ules </s> Figure ĠFIG REF 82 Ġ( see Ġthe ĠAppendix ) Ġdisplays Ġthe Ġentire Ġdialogue Ġflow . ĠIn Ġa Ġnutshell , Ġthe Ġsystem Ġreceives Ġa Ġuser Ġinput , Ġanaly zes Ġit Ġand Ġextracts Ġinformation , Ġif Ġprovided . ĠIf Ġsome Ġof Ġthe Ġrequired Ġinformation Ġis Ġmissing , Ġthe Ġsystem Ġasks Ġthe Ġstudent Ġto Ġprovide Ġit . ĠWhen Ġall Ġthe Ġinformation Ġis Ġcollected , Ġit Ġwill Ġbe Ġautomatically Ġvalidated Ġand Ġsubsequently Ġforwarded Ġto Ġa Ġhuman Ġtutor , Ġwho Ġthen Ġcan Ġdirectly Ġproceed Ġwith Ġthe Ġassistance . ĠIn Ġthe Ġfollowing Ġwe Ġwill Ġdescribe Ġthe Ġcentral Ġcomponents Ġof Ġthe Ġsystem . </s> OM B + ĠDesign : ĠFigure ĠFIG REF 52 Ġ( see ĠSection ĠSEC REF 11 Ġof Ġthe ĠAppendix ) Ġillustrates Ġthe Ġinternal Ġstructure Ġand Ġdesign Ġof Ġthe ĠO MB + Ġplatform . ĠIt Ġhas Ġtopics Ġand Ġsub - top ics , Ġas Ġwell Ġas Ġfour Ġexamination Ġmodes . ĠEach Ġtopic Ġ( Figure ĠFIG REF 52 , Ġtag Ġ1 ) Ġcorresponds Ġto Ġa Ġchapter Ġlevel Ġand Ġalways Ġhas Ġsub - top ics Ġ( Figure ĠFIG REF 52 , Ġtag Ġ2 ), Ġwhich Ġcorrespond Ġto Ġa Ġsection Ġlevel . ĠExamination Ġmodes Ġtraining Ġand Ġexercise Ġare Ġambiguous , Ġbecause Ġthey Ġcorrespond Ġto Ġeither Ġa Ġchapter Ġ( Figure ĠFIG REF 52 , Ġtag Ġ3 ) Ġor Ġa Ġsection Ġ( Figure ĠFIG REF 52 , Ġtag Ġ5 ) Ġlevel , Ġand Ġit Ġis Ġimportant Ġto Ġdifferentiate Ġbetween Ġthem , Ġsince Ġthey Ġcontain Ġdifferent Ġtypes Ġof Ġcontent . ĠThe Ġmode Ġfinal Ġexamination Ġ( Figure ĠFIG REF 52 , Ġtag Ġ4 ) Ġalways Ġcorresponds Ġto Ġa Ġchapter Ġlevel , Ġwhereas Ġquiz Ġ( Figure ĠFIG REF 52 , Ġtag Ġ5 ) Ġcan Ġbelong Ġonly Ġto Ġa Ġsection Ġlevel . ĠAccording Ġto Ġthe Ġdesign Ġof Ġthe ĠO MB + Ġplatform , Ġthere Ġare Ġseveral Ġways Ġof Ġhow Ġa Ġpossible Ġdialogue Ġflow Ġcan Ġproceed . </s> Pre processing : ĠIn Ġa Ġnatural Ġlanguage Ġdialogue , Ġa Ġuser Ġmay Ġrespond Ġin Ġmany Ġdifferent Ġways , Ġthus , Ġthe Ġextraction Ġof Ġany Ġdata Ġfrom Ġuser - generated Ġtext Ġis Ġa Ġchallenging Ġtask Ġdue Ġto Ġa Ġnumber Ġof Ġmiss pell ings Ġor Ġconf usable Ġspell ings Ġ( e . g ., ĠExercise Ġ1 . a , ĠExercise Ġ1 Ġ( a )). ĠTherefore , Ġto Ġenable Ġa Ġreliable Ġextraction Ġof Ġentities , Ġwe Ġpre process ed Ġand Ġnormalized Ġ( e . g ., Ġmiss pell ings , Ġsyn onyms ) Ġevery Ġuser Ġinput Ġbefore Ġit Ġwas Ġsent Ġto Ġthe ĠNatural ĠLanguage ĠUnderstanding Ġ( NL U ) Ġmodule . ĠThe Ġpre processing Ġincludes Ġfollowing Ġsteps : </s> lower c asing Ġand Ġstemming Ġof Ġall Ġwords Ġin Ġthe Ġinput ; </s> rem oval Ġof ĠGerman Ġstop Ġwords Ġand Ġpunct uation ; </s> all Ġmentions Ġof Ġ$ x $ Ġin Ġmathematical Ġformulas Ġwere Ġremoved Ġto Ġavoid Ġconfusion Ġwith Ġr oman Ġnumber Ġ10 Ġ( âĢ ľ $ X $ âĢ Ŀ ); </s> in Ġa Ġcombination Ġof Ġthe Ġtype : Ġword ĠâĢ ľ Chapter / Ex ercise âĢ Ŀ Ġ+ Ġdigit Ġwritten Ġas Ġa Ġword Ġ( i . e ĠâĢ ľ first âĢ Ŀ , ĠâĢ ľ second âĢ Ŀ ), Ġword Ġwas Ġreplaced Ġwith Ġa Ġdigit Ġ( âĢ ľ in Ġfirst ĠChapter âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ in ĠChapter Ġ1 âĢ Ŀ ), Ġr oman Ġnumbers Ġwere Ġreplaced Ġwith Ġdigits Ġas Ġwell Ġ( âĢ ľ Chapter ĠIV âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ Chapter Ġ4 âĢ Ŀ ). </s> det ected Ġambig u ities Ġwere Ġnormalized Ġ( e . g ., ĠâĢ ľ Train ings au fg abe âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ Training âĢ Ŀ ); </s> recogn ized Ġmiss pell ings Ġresp . Ġty pos Ġwere Ġcorrected Ġ( e . g ., ĠâĢ ľ D ife ern z ial re chn ung âĢ Ŀ Ġ$\ right arrow Ġ$ ĠâĢ ľ Different ial re chn ung ') ' </s> per mal inks Ġwere Ġparsed Ġand Ġanalyzed . ĠFrom Ġeach Ġper malink Ġit Ġis Ġpossible Ġto Ġextract Ġtopic , Ġexamination Ġmode Ġand Ġquestion Ġnumber ; </s> Natural ĠLanguage ĠUnderstanding Ġ( NL U ): ĠWe Ġimplemented Ġan ĠNL U Ġunit Ġutilizing Ġhand crafted Ġrules , ĠRegular ĠExpress ions Ġ( Reg Ex ) Ġand ĠElastic search Ġ( ES ) ĠAPI . ĠThe ĠNL U Ġmodule Ġcontains Ġfollowing Ġfunctional ities : </s> Int ent Ġclassification : ĠAs Ġwe Ġmentioned Ġabove , Ġstudent Ġquestions Ġcan Ġbe Ġgrouped Ġinto Ġthree Ġmain Ġcategories : ĠOrgan izational Ġquestions , Ġcontextual Ġquestions Ġand Ġmathematical Ġquestions . ĠTo Ġclassify Ġthe Ġinput Ġmessage Ġby Ġits Ġcategory Ġor Ġso - called Ġintent , Ġwe Ġutilized Ġkey - word Ġinformation Ġpred efined Ġby Ġhand crafted Ġrules . ĠWe Ġassumed Ġthat Ġparticular Ġwords Ġare Ġexplicit Ġand Ġassociated Ġwith Ġa Ġcorresponding Ġintent . ĠIf Ġno Ġintent Ġcould Ġbe Ġclassified , Ġthen Ġit Ġis Ġassumed Ġthat Ġthe ĠNL U Ġunit Ġwas Ġnot Ġcapable Ġof Ġunderstanding Ġand Ġthe Ġintent Ġis Ġinterpreted Ġas Ġunknown . ĠIn Ġthis Ġcase , Ġthe Ġsystem Ġrequests Ġthe Ġuser Ġto Ġprovide Ġan Ġintent Ġmanually Ġ( by Ġpicking Ġone Ġfrom Ġthe Ġmentioned Ġthree Ġoptions ). ĠThe Ġquestions Ġfrom Ġorganizational Ġand Ġtheoretical Ġcategories Ġare Ġdirectly Ġdelivered Ġto Ġa Ġhuman Ġtutor , Ġwhile Ġmathematical Ġquestions Ġare Ġprocessed Ġby Ġthe Ġautomated Ġsystem Ġfor Ġfurther Ġanalysis . </s> Entity ĠExt raction : ĠNext , Ġthe Ġsystem Ġattempts Ġto Ġretrieve Ġthe Ġentities Ġfrom Ġa Ġuser Ġmessage Ġon Ġthe Ġtopic Ġ( and Ġsub - topic ), Ġexamination Ġmode Ġand Ġlevel , Ġand Ġquestion Ġnumber . ĠThis Ġpart Ġis Ġimplemented Ġusing ĠElastic search Ġ( ES ) Ġand ĠReg Ex . ĠTo Ġenable Ġthe Ġuse Ġof ĠES , Ġwe Ġindexed Ġthe ĠO MB + Ġsite Ġto Ġan Ġinternal Ġdatabase . ĠBesides Ġindex ing Ġthe Ġtopics Ġand Ġtitles , Ġwe Ġalso Ġprovided Ġinformation Ġon Ġpossible Ġsyn onyms Ġor Ġwriting Ġstyles . ĠWe Ġadditionally Ġfiled ĠO MB + Ġper mal inks , Ġwhich Ġdirect Ġto Ġthe Ġsite Ġpages . ĠTo Ġquery Ġthe Ġresulting Ġdatabase , Ġwe Ġutilized Ġthe Ġinternal ĠElastic search Ġmulti Ġmatch Ġfunction Ġand Ġset Ġthe Ġminimum Ġshould Ġmatch Ġparameter Ġto Ġ$ 20 \ % $. ĠThis Ġparameter Ġdefines Ġthe Ġnumber Ġof Ġterms Ġthat Ġmust Ġmatch Ġfor Ġa Ġdocument Ġto Ġbe Ġconsidered Ġrelevant . ĠBesides Ġthat , Ġwe Ġadopted Ġfuzz iness Ġwith Ġthe Ġmaximum Ġedit Ġdistance Ġset Ġto Ġ2 Ġcharacters . ĠThe Ġfuzzy Ġquery Ġuses Ġsimilarity Ġbased Ġon ĠLe ven sh tein Ġedit Ġdistance ĠB IB REF 3 . ĠFinally , Ġthe Ġsystem Ġgenerates Ġa Ġranked Ġlist Ġof Ġpossible Ġmatching Ġentries Ġfound Ġin Ġthe Ġdatabase Ġwithin Ġthe Ġpred efined Ġrelevance Ġthreshold Ġ( we Ġset Ġit Ġto Ġ$\ the ta Ġ$ =$ 1 . 5 $ ). ĠWe Ġpick Ġthe Ġmost Ġprobable Ġentry Ġas Ġthe Ġcorrect Ġone Ġand Ġextract Ġthe Ġcorresponding Ġentity Ġfrom Ġthe Ġuser Ġinput . </s> To Ġsummarize , Ġthe ĠNL U Ġmodule Ġreceives Ġthe Ġuser Ġinput Ġas Ġa Ġpre process ed Ġtext Ġand Ġchecks Ġit Ġacross Ġall Ġpred efined ĠReg Ex Ġstatements Ġand Ġfor Ġa Ġmatch Ġin Ġthe ĠElastic search Ġdatabase . ĠEvery Ġtime Ġthe Ġentity Ġis Ġextracted , Ġit Ġis Ġentered Ġin Ġthe ĠInformation ĠDictionary Ġ( ID ). ĠThe ĠID Ġhas Ġthe Ġfollowing Ġsix Ġslots Ġto Ġbe Ġfilled Ġin : Ġtopic , Ġsub - topic , Ġexamination Ġlevel , Ġexamination Ġmode , Ġquestion Ġnumber , Ġand Ġexact Ġproblem Ġformulation . </s> Dialogue ĠManager Ġconsists Ġof Ġthe ĠDialogue ĠState ĠTracker Ġ( D ST ), Ġthat Ġmaintains Ġa Ġrepresentation Ġof Ġthe Ġcurrent Ġdialog Ġstate , Ġand Ġof Ġthe ĠPolicy ĠLear ner Ġ( PL ) Ġthat Ġdefines Ġthe Ġnext Ġsystem Ġaction . ĠIn Ġour Ġmodel , Ġthe Ġsystem 's Ġnext Ġaction Ġis Ġdefined Ġby Ġthe Ġstate Ġof Ġthe Ġpreviously Ġobtained Ġinformation Ġstored Ġin Ġthe ĠInformation ĠDictionary . ĠFor Ġinstance , Ġif Ġthe Ġsystem Ġrecognizes Ġthat Ġthe Ġstudent Ġworks Ġon Ġthe Ġfinal Ġexamination , Ġit Ġalso Ġunderstands Ġ( defined Ġby Ġthe Ġlogic Ġin Ġthe Ġpred efined Ġrules ) Ġthat Ġthere Ġis Ġno Ġneed Ġto Ġask Ġfor Ġsub - topic Ġbecause Ġthe Ġfinal Ġexamination Ġalways Ġcorresponds Ġto Ġa Ġchapter Ġlevel Ġ( due Ġto Ġthe Ġdesign Ġof ĠO MB + Ġplatform ). ĠIf Ġthe Ġsystem Ġidentifies Ġthat Ġthe Ġuser Ġhas Ġdifficulties Ġin Ġsolving Ġa Ġquiz , Ġit Ġhas Ġto Ġask Ġfor Ġthe Ġcorresponding Ġtopic Ġand Ġsub - topic Ġif Ġnot Ġyet Ġprovided Ġby Ġa Ġuser Ġ( because Ġthe Ġquiz Ġalways Ġrefers Ġto Ġa Ġsection Ġlevel ). ĠTo Ġdetermine Ġall Ġof Ġthe Ġpotential Ġdialogue Ġflows , Ġwe Ġimplemented ĠMut ually ĠExclusive ĠRules Ġ( MER ), Ġwhich Ġindicate Ġthat Ġtwo Ġevents Ġ$ e _{ 1 } $ Ġand Ġ$ e _{ 2 } $ Ġare Ġmutually Ġexclusive Ġor Ġdis j oint Ġif Ġthey Ġcannot Ġboth Ġoccur Ġat Ġthe Ġsame Ġtime Ġ( thus , Ġthe Ġintersection Ġof Ġthese Ġevents Ġis Ġempty : Ġ$ P ( A Ġ\ cap ĠB ) Ġ= Ġ0 $ ). ĠAdditionally , Ġwe Ġdefined Ġtransition Ġand Ġmapping Ġrules . ĠThe Ġformal Ġexplanation Ġof Ġrules Ġcan Ġbe Ġfound Ġin ĠSection ĠSEC REF 12 Ġof Ġthe ĠAppendix . ĠFollowing Ġthe Ġrules , Ġwe Ġgenerated Ġ56 Ġstate Ġtransitions , Ġwhich Ġdefine Ġnext Ġsystem Ġactions . ĠBeing Ġon Ġa Ġnew Ġdialogue Ġstate , Ġthe Ġsystem Ġcompares Ġthe Ġextracted Ġ( i . e ., Ġupdated ) Ġinformation Ġin Ġthe ĠID Ġwith Ġthe Ġvalid Ġdialogue Ġstates Ġ( see ĠSection ĠSEC REF 12 Ġof Ġthe ĠAppendix Ġfor Ġthe Ġexplanation Ġof Ġthe Ġvalid ness ) Ġand Ġpicks Ġthe Ġmapped Ġaction Ġas Ġthe Ġnext Ġsystem 's Ġaction . </s> The Ġabove mentioned Ġrules Ġare Ġintended Ġto Ġsupport Ġthe Ġcurrent Ġdesign Ġof Ġthe ĠO MB + Ġlearning Ġplatform . ĠHowever , Ġadditional ĠMER s Ġcould Ġbe Ġadded Ġto Ġgenerate Ġnew Ġtransitions . ĠEx empl ifying Ġthis , Ġwe Ġconducted Ġexperiments Ġwith Ġthe Ġformer Ġdesign Ġof Ġthe Ġplatform , Ġwhere Ġall Ġthe Ġtopics , Ġexcept Ġfor Ġthe Ġfirst Ġone , Ġhad Ġonly Ġsub - top ics , Ġwhereas Ġthe Ġfirst Ġtopic Ġhad Ġboth Ġsub - top ics Ġand Ġsub - sub - top ics . ĠWe Ġcould Ġeffortlessly Ġgenerate Ġthe Ġmissing Ġtransitions Ġwith Ġour Ġapproach . ĠThe Ġnumber Ġof Ġpossible Ġtransitions , Ġin Ġthis Ġcase , Ġincreased Ġfrom Ġ56 Ġto Ġ117 . </s> Meta ĠPolicy : ĠSince Ġthe Ġsystem Ġis Ġintended Ġto Ġoperate Ġin Ġa Ġreal - world Ġscenario , Ġwe Ġhad Ġto Ġimplement Ġadditional Ġpolicies Ġthat Ġcontrol Ġthe Ġdialogue Ġflow Ġand Ġvalidate Ġthe Ġsystem 's Ġaccuracy . ĠBelow Ġwe Ġdescribe Ġthese Ġpolicies : </s> Com ple teness Ġof Ġthe ĠInformation ĠDictionary : ĠIn Ġthis Ġstep , Ġthe Ġsystem Ġvalid ates Ġthe Ġcomple teness Ġof Ġthe ĠID , Ġwhich Ġis Ġdefined Ġby Ġthe Ġnumber Ġof Ġobligatory Ġslots Ġfilled Ġin Ġthe Ġinformation Ġdictionary . ĠThere Ġare Ġ6 Ġdistinct Ġcases Ġwhen Ġthe ĠID Ġis Ġconsidered Ġto Ġbe Ġcomplete Ġ( see ĠSection ĠSEC REF 13 Ġof Ġthe ĠAppendix ). ĠFor Ġinstance , Ġif Ġa Ġuser Ġworks Ġon Ġa Ġfinal Ġexamination , Ġthe Ġsystem Ġdoes Ġnot Ġhas Ġto Ġrequest Ġa Ġsub - topic Ġor Ġexamination Ġlevel . ĠThus , Ġthe ĠID Ġhas Ġto Ġbe Ġfilled Ġonly Ġwith Ġdata Ġfor Ġa Ġtopic , Ġexamination Ġmode , Ġand Ġquestion Ġnumber , Ġwhereas , Ġif Ġthe Ġuser Ġworks Ġon Ġa Ġquiz , Ġthe Ġsystem Ġhas Ġto Ġgather Ġinformation Ġabout Ġthe Ġtopic , Ġsub - topic , Ġexamination Ġmode , Ġand Ġthe Ġquestion Ġnumber . ĠOnce Ġthe ĠID Ġis Ġcomplete , Ġit Ġis Ġprovided Ġto Ġthe Ġverification Ġstep . ĠOtherwise , Ġthe Ġsystem Ġproceeds Ġaccording Ġto Ġthe Ġnext Ġaction . ĠThe Ġsystem Ġextracts Ġthe Ġinformation Ġin Ġeach Ġdialogue Ġstep , Ġand Ġthus Ġif Ġthe Ġuser Ġprovides Ġupdated Ġinformation Ġon Ġany Ġsubject Ġlater Ġin Ġthe Ġdialogue , Ġthe Ġcorresponding Ġslot Ġwill Ġbe Ġupdated Ġin Ġthe ĠID . </s> Ver ification ĠStep : ĠOnce Ġthe Ġsystem Ġhas Ġobtained Ġall Ġthe Ġnecessary Ġinformation Ġ( i . e ., ĠID Ġis Ġcomplete ), Ġit Ġproceeds Ġto Ġthe Ġfinal Ġverification . ĠIn Ġthat Ġstep , Ġthe Ġcollected Ġdata Ġis Ġshown Ġto Ġthe Ġstudent Ġin Ġthe Ġsession . ĠThe Ġstudent Ġis Ġasked Ġto Ġverify Ġthe Ġcorrectness Ġof Ġthe Ġcollected Ġdata Ġand Ġif Ġsome Ġentries Ġare Ġwrong , Ġto Ġcorrect Ġthem . ĠThe ĠID Ġis , Ġwhere Ġnecessary , Ġupdated Ġwith Ġthe Ġuser - provided Ġdata . ĠThis Ġprocedure Ġrepeats Ġuntil Ġthe Ġuser Ġconfirms Ġthe Ġcorrectness Ġof Ġthe Ġassembled Ġdata . </s> Fall back ĠPolicy : ĠIn Ġsome Ġcases , Ġthe Ġsystem Ġfails Ġto Ġderive Ġinformation Ġfrom Ġa Ġstudent Ġquery , Ġeven Ġif Ġthe Ġstudent Ġprovides Ġit . ĠIt Ġis Ġdue Ġto Ġthe ĠElastic search Ġfunctionality Ġand Ġpreviously Ġunseen ĠReg Ex Ġpatterns . ĠIn Ġthese Ġcases , Ġthe Ġsystem Ġre - asks Ġa Ġuser Ġand Ġattempts Ġto Ġretrieve Ġinformation Ġfrom Ġa Ġfollow - up Ġquery . ĠThe Ġmaximum Ġnumber Ġof Ġre - ask Ġattempts Ġis Ġset Ġto Ġthree Ġtimes Ġ($ r = 3 $ ). ĠIf Ġthe Ġsystem Ġis Ġunable Ġto Ġextract Ġinformation Ġafter Ġthree Ġtimes , Ġthe Ġuser Ġinput Ġis Ġconsidered Ġas Ġthe Ġground Ġtruth Ġand Ġsaved Ġto Ġthe Ġappropriate Ġslot Ġin ĠID . ĠAn Ġexception Ġto Ġthis Ġrule Ġapplies Ġwhere Ġthe Ġuser Ġhas Ġto Ġspecify Ġthe Ġintent Ġmanually . ĠIn Ġthis Ġcase , Ġafter Ġthree Ġun classified Ġattempts , Ġa Ġsession Ġis Ġdirectly Ġhanded Ġover Ġto Ġa Ġhuman Ġtutor . </s> Human ĠRequest : ĠIn Ġeach Ġdialogue Ġstate , Ġa Ġuser Ġcan Ġswitch Ġto Ġa Ġhuman Ġtutor . ĠFor Ġthis , Ġa Ġuser Ġcan Ġenter Ġthe Ġhuman Ġkey - word . ĠHence , Ġevery Ġuser Ġmessage Ġis Ġadditionally Ġanalyzed Ġfor Ġthe Ġpresence Ġof Ġthis Ġkey - word . </s> Response ĠGeneration : ĠIn Ġthis Ġmodule , Ġthe Ġsemantic Ġrepresentation Ġof Ġthe Ġsystem 's Ġnext Ġaction Ġis Ġtransformed Ġinto Ġnatural Ġlanguage . ĠHence , Ġeach Ġpossible Ġaction Ġis Ġmapped Ġto Ġprecisely Ġone Ġutter ance , Ġwhich Ġis Ġstored Ġin Ġthe Ġtemplates . ĠSome Ġof Ġthe Ġpred efined Ġresponses Ġare Ġfixed Ġ( i . e ., ĠâĢ ľ W el ches ĠKap it el Ġbear beit est Ġdu Ġger ade ? âĢ Ŀ ), Ġothers Ġhave Ġplace holders Ġfor Ġsystem Ġvalues . ĠIn Ġthe Ġlatter Ġcase , Ġthe Ġutter ance Ġcan Ġbe Ġformulated Ġdependent Ġon Ġthe Ġactual ĠID . ĠThe Ġdialogue Ġshowcases Ġcan Ġbe Ġfound Ġin ĠSection ĠSEC REF 14 Ġof Ġthe ĠAppendix . </s> E val uation </s> In Ġorder Ġto Ġget Ġthe Ġfeedback Ġon Ġthe Ġquality , Ġfunctionality , Ġand Ġusefulness Ġof Ġthe Ġintroduced Ġmodel , Ġwe Ġevaluated Ġit Ġin Ġtwo Ġways : Ġfirst , Ġwith Ġan Ġautomated Ġmethod Ġusing Ġ130 Ġmanually Ġannot ated Ġdialog ues , Ġto Ġprove Ġthe Ġrobust ness Ġof Ġthe Ġsystem , Ġand Ġsecond ĠâĢĵ Ġwith Ġhuman Ġtut ors Ġfrom ĠO MB + ĠâĢĵ Ġto Ġinvestigate Ġthe Ġuser Ġexperience . ĠWe Ġdescribe Ġthe Ġdetails Ġas Ġwell Ġas Ġthe Ġmost Ġcommon Ġerrors Ġbelow . </s> E val uation Ġ: :: ĠAutom ated ĠEvaluation </s> To Ġconduct Ġan Ġautomated Ġevaluation , Ġwe Ġmanually Ġcreated Ġa Ġdataset Ġwith Ġ130 Ġdialog Ġscenarios . ĠWe Ġtook Ġreal Ġfirst Ġuser Ġquestions Ġand Ġpred efined Ġpossible Ġuser Ġresponses , Ġas Ġwell Ġas Ġgold Ġsystem Ġanswers . ĠThese Ġscenarios Ġcover Ġmost Ġfrequent Ġdialog ues Ġthat Ġwe Ġpreviously Ġsaw Ġin Ġhuman - to - human Ġdialogue Ġdata . ĠWe Ġtested Ġour Ġsystem Ġby Ġimplementing Ġa Ġself - chat ting Ġevaluation Ġbot . ĠThe Ġevaluation Ġcycle Ġcould Ġbe Ġdescribed Ġas Ġfollows : </s> The Ġsystem Ġreceives Ġthe Ġfirst Ġquestion Ġfrom Ġa Ġpred efined Ġdialogue Ġvia Ġan ĠAPI Ġrequest , Ġpre process es Ġand Ġanaly zes Ġit Ġin Ġorder Ġto Ġextract Ġentities . </s> Then Ġit Ġestimates Ġthe Ġapplicable Ġnext Ġaction , Ġand Ġresponds Ġaccording Ġto Ġit . </s> This Ġresponse Ġis Ġthen Ġcompared Ġto Ġthe Ġsystem 's Ġgold Ġanswer : ĠIf Ġthe Ġpredicted Ġanswer Ġis Ġcorrect , Ġthen Ġthe Ġsystem Ġreceives Ġthe Ġnext Ġpred efined Ġuser Ġinput Ġfrom Ġthe Ġdialog Ġand Ġresponds Ġagain , Ġas Ġdefined Ġabove . ĠThis Ġprocedure Ġcontinues Ġuntil Ġthe Ġdialog Ġtermin ates Ġ( i . e ., ĠID Ġis Ġcomplete ). ĠOtherwise , Ġthe Ġsystem Ġfails , Ġreporting Ġthe Ġunsuccessful Ġcase Ġnumber . </s> Our Ġfinal Ġsystem Ġsuccessfully Ġpassed Ġthis Ġevaluation Ġfor Ġall Ġ130 Ġcases . </s> E val uation Ġ: :: ĠHuman ĠEvaluation Ġand ĠError ĠAnalysis </s> To Ġevaluate Ġour Ġsystem Ġon Ġirregular Ġexamples , Ġwe Ġconducted Ġexperiments Ġwith Ġhuman Ġtut ors Ġfrom Ġthe ĠO MB + Ġplatform . ĠThe Ġtut ors Ġare Ġexperienced Ġregarding Ġrare Ġor Ġcomplex Ġquestions , Ġambiguous Ġanswers , Ġmiss pell ings Ġand Ġother Ġinf requent Ġbut Ġstill Ġvery Ġrelevant Ġproblems , Ġwhich Ġoccur Ġduring Ġa Ġdialogue . ĠIn Ġthe Ġfollowing , Ġwe Ġinvestigate Ġsome Ġcommon Ġerrors Ġand Ġmake Ġadditional Ġobservations . </s> Miss pell ings Ġand Ġconf usable Ġspell ings Ġoccur Ġquite Ġoften Ġin Ġthe Ġuser - generated Ġtext , Ġand Ġsince Ġwe Ġattempt Ġto Ġlet Ġthe Ġconversation Ġremain Ġvery Ġnatural Ġfrom Ġthe Ġuser Ġside Ġand Ġthus , Ġcannot Ġrequire Ġformal Ġwriting , Ġwe Ġhave Ġto Ġdeal Ġwith Ġvarious Ġwriting Ġissues . ĠOne Ġof Ġthe Ġmost Ġfrequent Ġproblems Ġis Ġmiss pell ings . ĠGerman Ġwords Ġare Ġgenerally Ġlong Ġand Ġcan Ġbe Ġcomplicated , Ġand Ġsince Ġusers Ġtype Ġquickly , Ġthis Ġoften Ġleads Ġto Ġthe Ġwrong Ġorder Ġof Ġcharacters Ġwithin Ġwords . ĠTo Ġtackle Ġthis Ġchallenge , Ġwe Ġused Ġfuzzy Ġmatch Ġwithin ĠES . ĠHowever , Ġthe Ġmaximum Ġallowed Ġedit Ġdistance Ġin ĠElastic search Ġis Ġset Ġto Ġ2 Ġcharacters . ĠThis Ġmeans , Ġthat Ġall Ġthe Ġmiss pell ings Ġbeyond Ġthis Ġthreshold Ġcould Ġnot Ġbe Ġaccurately Ġrecognized Ġby ĠES Ġ( e . g ., ĠDifferent ial re chn ung Ġvs ĠDiff er ne z ial re chn ung ). ĠAnother Ġcharacteristic Ġexample Ġwould Ġbe Ġthe Ġwriting Ġof Ġthe Ġsection Ġor Ġquestion Ġnumber . ĠThe Ġequivalent Ġinformation Ġcan Ġbe Ġwritten Ġin Ġseveral Ġdistinct Ġways , Ġwhich Ġhas Ġto Ġbe Ġconsidered Ġin Ġour ĠReg Ex Ġunit Ġ( e . g ., ĠExercise Ġ5 Ġa , ĠExercise ĠV Ġa , ĠExercise Ġ5 Ġ( a )). ĠA Ġsimilar Ġproblem Ġoccurs Ġwith Ġconf usable Ġspelling Ġ( i . e .: ĠDifferent ial re chn ung Ġvs ĠDifferent ial g le ich ung ). ĠWe Ġanalyzed Ġthe Ġcases Ġmentioned Ġabove Ġand Ġadded Ġsome Ġof Ġthe Ġmost Ġcommon Ġissues Ġto Ġthe ĠES Ġdatabase Ġor Ġhandled Ġthem Ġwith ĠReg Ex Ġduring Ġthe Ġpre processing Ġstep . </s> El astic search ĠTh reshold : ĠIn Ġsome Ġcases , Ġthe Ġsystem Ġfailed Ġto Ġextract Ġinformation , Ġalthough Ġthe Ġuser Ġprovided Ġit . ĠIn Ġother Ġcases , ĠES Ġextracts Ġinformation Ġthat Ġwas Ġnot Ġmentioned Ġin Ġa Ġuser Ġquery Ġat Ġall . ĠThat Ġoccurs Ġdue Ġto Ġthe Ġrele v ancy Ġscoring Ġalgorithm Ġof ĠElastic search , Ġwhere Ġa Ġdocument 's Ġscore Ġis Ġa Ġcombination Ġof Ġtextual Ġsimilarity Ġand Ġother Ġmetadata Ġbased Ġscores . ĠOur Ġanalysis Ġrevealed Ġthat ĠES Ġmostly Ġfails Ġto Ġextract Ġthe Ġinformation Ġif Ġthe Ġsentence Ġ( i . e ., Ġuser Ġmessage ) Ġis Ġquite Ġshort Ġ( e . g ., Ġ5 Ġwords ). ĠTo Ġovercome Ġthis Ġdifficulty , Ġwe Ġcombined Ġthe Ġcurrent Ġinput Ġ$ u _{ t } $ Ġwith Ġthe Ġdialog Ġhistory . ĠThis Ġstep Ġeliminated Ġthe Ġproblem Ġand Ġimproved Ġthe Ġretrieval Ġquality . ĠTo Ġsolve Ġthe Ġcase Ġwhere ĠElastic search Ġextracts Ġincorrect Ġinformation Ġ( or Ġinformation Ġthat Ġwas Ġnot Ġmentioned Ġin Ġa Ġquery ) Ġis Ġmore Ġchallenging . ĠWe Ġdiscovered Ġthat Ġthe Ġproblem Ġcomes Ġfrom Ġshort Ġwords Ġor Ġsub - words Ġ( e . g ., Ġsuffix es , Ġprefix es ), Ġwhich ĠES Ġconsiders Ġto Ġbe Ġcredible Ġenough . ĠThe ĠElastic search Ġdocumentation Ġsuggests Ġgetting Ġrid Ġof Ġstop Ġwords Ġto Ġeliminate Ġthis Ġbehavior . ĠHowever , Ġthis Ġdid Ġnot Ġimprove Ġthe Ġsearch Ġin Ġour Ġcase . ĠAlso , Ġfine - tun ing Ġof ĠES Ġparameters Ġsuch Ġas Ġthe Ġrelevance Ġthreshold , Ġprefix Ġlength Ġand Ġminimum Ġshould Ġmatch Ġparameter Ġdid Ġnot Ġbring Ġsignificant Ġimprovements . ĠTo Ġcope Ġwith Ġthis Ġproblem , Ġwe Ġimplemented Ġa Ġverification Ġstep , Ġwhere Ġa Ġuser Ġis Ġgiven Ġa Ġchance Ġto Ġcorrect Ġthe Ġerrone ously Ġretrieved Ġinformation . </s> The Ġoverall Ġfeedback Ġfrom Ġthe Ġtut ors Ġincluded Ġreduced Ġrepetitive Ġactivities Ġas Ġwell Ġas Ġreduced Ġwaiting Ġtimes Ġfor Ġstudents Ġuntil Ġtheir Ġquestions Ġwere Ġprocessed . ĠAlso , Ġtut ors Ġreported Ġthat Ġthe Ġrate Ġof Ġcancelled Ġsessions Ġ( sw itching Ġto Ġa Ġhuman Ġtutor ) Ġis Ġrather Ġlow . </s> Struct ured ĠDialogue ĠAcquisition </s> As Ġwe Ġalready Ġmentioned , Ġour Ġsystem Ġattempts Ġto Ġsupport Ġthe Ġhuman Ġtutor Ġby Ġassisting Ġstudents , Ġbut Ġit Ġalso Ġcollects Ġstructured Ġand Ġlabeled Ġtraining Ġdata Ġin Ġthe Ġbackground . ĠIn Ġa Ġtrial Ġrun Ġof Ġthe Ġrule - based Ġsystem , Ġwe Ġwere Ġable Ġto Ġaccumulate Ġa Ġtoy - dat as et Ġwith Ġtraining Ġdialog ues . ĠThe Ġassembled Ġdialog ues Ġhave Ġthe Ġfollowing Ġformat : </s> Pl ain Ġdialog ues Ġwith Ġunique Ġdialogue Ġindexes ; </s> Pl ain ĠInformation ĠDictionary Ġinformation Ġ( e . g ., Ġextracted Ġentities ) Ġcollected Ġfor Ġthe Ġwhole Ġdialogue ; </s> P airs Ġof Ġquestions Ġ( i . e ., Ġuser Ġrequests ) Ġand Ġresponses Ġ( i . e ., Ġbot Ġresponses ) Ġwith Ġthe Ġunique Ġdialogue - Ġand Ġturn - index es ; </s> Tri ples Ġin Ġthe Ġform Ġof Ġ( User ĠRequest , ĠNext ĠAction , ĠResponse ). ĠInformation Ġon Ġthe Ġnext Ġsystem 's Ġaction Ġcould Ġbe Ġemployed Ġto Ġtrain Ġa ĠDialogue ĠManager Ġunit Ġwith Ġ( deep -) Ġmachine Ġlearning Ġalgorithms ; </s> For Ġeach Ġstate Ġin Ġthe Ġdialogue , Ġwe Ġsaved Ġthe Ġentities Ġthat Ġthe Ġsystem Ġwas Ġable Ġto Ġextract Ġfrom Ġthe Ġprovided Ġuser Ġquery , Ġalong Ġwith Ġtheir Ġposition Ġin Ġthe Ġutter ance . ĠThis Ġinformation Ġcould Ġbe Ġused Ġto Ġtrain Ġa Ġcustom , Ġdomain Ġspecific ĠNamed ĠEntity ĠRecogn ition Ġmodel . </s> Re - im plementation Ġof Ġunits Ġwith ĠB ERT </s> As Ġwe Ġmentioned Ġbefore , Ġthere Ġare Ġmany Ġcases , Ġespecially Ġin Ġthe Ġindustry , Ġwhere Ġthe Ġlabeled Ġand Ġstructured Ġdata Ġis Ġnot Ġdirectly Ġavailable . ĠCollect ing Ġand Ġlabeling Ġsuch Ġdata Ġis Ġoften Ġa Ġtedious Ġand Ġtime - consuming Ġtask . ĠThus , Ġalgorithms Ġthat Ġenable Ġtraining Ġof Ġthe Ġsystems Ġwith Ġless Ġor Ġeven Ġa Ġminimal Ġamount Ġof Ġdata Ġare Ġhighly Ġrequired . ĠSuch Ġalgorithms Ġcan Ġtransfer Ġthe Ġknowledge Ġobtained Ġduring Ġthe Ġtraining Ġon Ġexisting Ġdata Ġto Ġthe Ġunseen Ġdomain . ĠThey Ġare , Ġtherefore , Ġone Ġof Ġthe Ġpotential Ġsolutions Ġfor Ġindustrial Ġproblems . </s> Once Ġwe Ġassembled Ġa Ġdataset Ġof Ġstructured Ġdata Ġvia Ġour Ġrule - based Ġsystem , Ġwe Ġre - im ple mented Ġtwo Ġout Ġof Ġthree Ġcentral Ġdialogue Ġcomponents Ġin Ġour Ġconvers ational Ġassistant Ġwith Ġdeep Ġlearning Ġmethods . ĠSince Ġthe Ġavailable Ġdata Ġwas Ġcollected Ġin Ġa Ġtrial - run Ġand Ġthus Ġthe Ġobtained Ġdataset Ġwas Ġrather Ġsmall Ġto Ġtrain Ġa Ġmachine Ġlearning Ġmodel Ġfrom Ġscratch , Ġwe Ġutilized Ġthe ĠTransfer ĠLearning Ġapproach , Ġand Ġfine - tun ed Ġthe Ġexisting Ġpre - trained Ġmodel Ġ( i . e ., ĠB ERT ) Ġfor Ġour Ġtarget Ġdomain Ġand Ġdata . </s> For Ġthe Ġexperiments , Ġwe Ġdefined Ġtwo Ġtasks : </s> First , Ġwe Ġstudied Ġthe ĠNamed ĠEntity ĠRecogn ition Ġproblem Ġin Ġa Ġcustom Ġdomain Ġsetting . ĠWe Ġdefined Ġa Ġsequence Ġlabeling Ġtask Ġand Ġemployed Ġthe ĠB ERT Ġmodel ĠB IB REF 4 . ĠWe Ġapplied Ġthe Ġmodel Ġto Ġour Ġdataset Ġand Ġfine - tun ed Ġit Ġfor Ġsix Ġ( 6 ) Ġdomain - specific Ġ( i . e ., Ġe - learning ) Ġentities Ġand Ġone Ġ( 1 ) ĠâĢ ľ unknown âĢ Ŀ Ġlabel . </s> Second , Ġwe Ġinvestigated Ġthe Ġeffectiveness Ġof ĠB ERT Ġfor Ġthe Ġdialogue Ġmanager Ġcore . ĠFor Ġthat Ġexperiment , Ġwe Ġdefined Ġa Ġclassification Ġtask Ġand Ġapplied Ġthe Ġmodel Ġto Ġpredict Ġthe Ġsystem 's ĠNext ĠAction Ġfor Ġevery Ġgiven Ġuser Ġutter ance Ġin Ġa Ġconversation . ĠWe Ġthen Ġcomputed Ġthe Ġmacro ĠF - score Ġfor Ġ13 Ġpossible Ġactions Ġand Ġan Ġaverage Ġdialogue Ġaccuracy . </s> Finally , Ġwe Ġverified Ġthat Ġthe Ġapplied Ġmodel Ġperformed Ġwell Ġon Ġboth Ġtasks : ĠWe Ġachieved Ġthe Ġperformance Ġof Ġ$ 0 . 93 $ Ġmacro ĠF 1 Ġpoints Ġfor ĠNamed ĠEntity ĠRecogn ition Ġ( NER ) Ġand Ġ$ 0 . 75 $ Ġmacro ĠF 1 Ġpoints Ġfor Ġthe ĠNext ĠAction ĠPrediction Ġ( N AP ) Ġtask . ĠWe , Ġtherefore , Ġconclude Ġthat Ġboth ĠN ER Ġand ĠN AP Ġcomponents Ġcould Ġbe Ġemployed Ġto Ġsubstitute Ġor Ġextend Ġthe Ġexisting Ġrule - based Ġmodules . </s> Data Ġ& ĠDes cript ive ĠStatistics : ĠThe Ġdataset Ġthat Ġwe Ġcollected Ġduring Ġthe Ġtrial - run Ġconsists Ġof Ġ300 Ġstructured Ġdialog ues Ġwith Ġthe Ġaverage Ġlength Ġof Ġa Ġdialogue Ġbeing Ġsix Ġ( 6 ) Ġutter ances . ĠCommunication Ġwith Ġstudents Ġwas Ġperformed Ġin Ġthe ĠGerman Ġlanguage . ĠDet ailed Ġgeneral Ġstatistics Ġcan Ġbe Ġfound Ġin ĠTable ĠT AB REF 41 . </s> N amed ĠEntity ĠRecogn ition : ĠWe Ġdefined Ġa Ġsequence Ġlabeling Ġtask Ġto Ġextract Ġcustom Ġentities Ġfrom Ġuser Ġinput . ĠWe Ġassumed Ġseven Ġ( 7 ) Ġpossible Ġentities Ġ( see ĠTable ĠT AB REF 43 ) Ġto Ġbe Ġrecognized Ġby Ġthe Ġmodel : Ġtopic , Ġsub topic , Ġexamination Ġmode Ġand Ġlevel , Ġquestion Ġnumber , Ġintent , Ġas Ġwell Ġas Ġthe Ġentity Ġother Ġfor Ġremaining Ġwords Ġin Ġthe Ġutter ance . ĠSince Ġthe Ġdata Ġobtained Ġfrom Ġthe Ġrule - based Ġsystem Ġalready Ġcontains Ġinformation Ġon Ġthe Ġentities Ġextracted Ġfrom Ġeach Ġuser Ġquery Ġ( i . e ., Ġby Ġmeans Ġof ĠElastic search ), Ġwe Ġcould Ġuse Ġit Ġto Ġtrain Ġa Ġdomain - specific ĠN ER Ġunit . ĠHowever , Ġsince Ġthe Ġuser - input Ġwas Ġinformal , Ġthe Ġsame Ġinformation Ġcould Ġbe Ġprovided Ġin Ġdifferent Ġwriting Ġstyles . ĠThat Ġmeans Ġthat Ġa Ġsingle Ġentity Ġcould Ġhave Ġdifferent Ġsurface Ġforms Ġ( e . g ., Ġsyn onyms , Ġwriting Ġstyles ) Ġ( although Ġentities Ġthat Ġwe Ġextracted Ġfrom Ġthe Ġrule - based Ġsystem Ġwere Ġall Ġconverted Ġto Ġa Ġuniversal Ġstandard , Ġe . g ., Ġofficial Ġchapter Ġnames ). ĠTo Ġconsider Ġall Ġof Ġthe Ġvariable Ġentity Ġforms Ġwhile Ġpost - label ing Ġthe Ġoriginal Ġdataset , Ġwe Ġdefined Ġgeneric Ġentity Ġnames Ġ( e . g ., Ġchapter , Ġquestion Ġn r .) Ġand Ġmapped Ġvariations Ġof Ġentities Ġfrom Ġthe Ġuser Ġinput Ġ( e . g ., ĠChapter Ġ= Ġ[ Element ary ĠCal culus , ĠChapter Ġ$ I $, Ġ... ]) Ġto Ġthem . </s> Next ĠAction ĠPrediction : ĠWe Ġdefined Ġa Ġclassification Ġproblem Ġto Ġpredict Ġthe Ġsystem 's Ġnext Ġaction Ġaccording Ġto Ġthe Ġgiven Ġuser Ġinput . ĠWe Ġassumed Ġ13 Ġcustom Ġactions Ġ( see ĠTable ĠT AB REF 42 ) Ġthat Ġwe Ġconsidered Ġbeing Ġour Ġlabels . ĠIn Ġthe Ġconvers ational Ġdataset , Ġeach Ġinput Ġwas Ġautomatically Ġlabeled Ġby Ġthe Ġrule - based Ġsystem Ġwith Ġthe Ġcorresponding Ġnext Ġaction Ġand Ġthe Ġdialogue - id . ĠThus , Ġno Ġadditional Ġpost - label ing Ġwas Ġrequired . ĠWe Ġinvestigated Ġtwo Ġsettings : </s> Default ĠSetting : ĠUsing Ġonly Ġa Ġuser Ġinput Ġand Ġthe Ġcorresponding Ġlabel Ġ( i . e ., Ġnext Ġaction ) Ġwithout Ġadditional Ġcontext . ĠBy Ġdefault , Ġwe Ġrun Ġall Ġof Ġour Ġexperiments Ġin Ġthis Ġsetting . </s> Ext ended ĠSetting : ĠUsing Ġa Ġuser Ġinput , Ġa Ġcorresponding Ġnext Ġaction , Ġand Ġa Ġprevious Ġsystem Ġaction Ġas Ġa Ġsource Ġof Ġadditional Ġcontext . ĠFor Ġthis Ġsetting , Ġwe Ġrun Ġan Ġexperiment Ġwith Ġthe Ġbest Ġperforming Ġmodel Ġfrom Ġthe Ġdefault Ġsetting . </s> The Ġoverall Ġdataset Ġconsists Ġof Ġ300 Ġlabeled Ġdialog ues , Ġwhere Ġ200 Ġ( with Ġ11 61 Ġutter ances ) Ġof Ġthem Ġwere Ġemployed Ġfor Ġtraining , Ġand Ġ100 Ġfor Ġevaluation Ġand Ġtest Ġsets Ġ( 50 Ġdialog ues Ġwith Ġabout Ġ300 Ġutter ances Ġfor Ġeach Ġset Ġrespectively ). </s> Model ĠSettings : ĠFor Ġthe ĠN ER Ġtask Ġwe Ġconducted Ġexperiments Ġwith ĠGerman Ġand Ġmult ilingual ĠB ERT Ġimplementations . ĠSince Ġin Ġthe ĠGerman Ġlanguage Ġthe Ġcapital ization Ġof Ġwords Ġplays Ġa Ġsignificant Ġrole , Ġwe Ġrun Ġour Ġtests Ġon Ġthe Ġcapital ized Ġinput , Ġwhile Ġkeeping Ġthe Ġoriginal Ġpunct uation . ĠHence , Ġwe Ġemployed Ġthe Ġavailable Ġbase Ġmodel Ġfor Ġboth Ġmult ilingual Ġand ĠGerman ĠB ERT Ġimplementations Ġin Ġthe Ġc ased Ġversion . ĠWe Ġset Ġthe Ġlearning Ġrate Ġfor Ġboth Ġmodels Ġto Ġ$ 1 e - 4 $ Ġand Ġthe Ġmaximum Ġlength Ġof Ġthe Ġtoken ized Ġinput Ġwas Ġset Ġto Ġ128 Ġtokens . ĠWe Ġrun Ġthe Ġexperiments Ġmultiple Ġtimes Ġwith Ġdifferent Ġseeds Ġfor Ġa Ġmaximum Ġof Ġ50 Ġepoch s Ġwith Ġthe Ġtraining Ġbatch Ġsize Ġset Ġto Ġ32 . ĠWe Ġutilized ĠAdam W Ġas Ġthe Ġoptim izer Ġand Ġemployed Ġearly Ġstopping , Ġif Ġthe Ġperformance Ġdid Ġnot Ġchange Ġsignificantly Ġafter Ġ5 Ġepoch s . </s> For Ġthe ĠN AP Ġtask Ġwe Ġconducted Ġexperiments Ġwith ĠGerman Ġand Ġmult ilingual ĠB ERT Ġimplementations Ġas Ġwell . ĠHere , Ġwe Ġinvestigated Ġthe Ġperformance Ġof Ġboth Ġcapital ized Ġand Ġlower c ased Ġinput , Ġas Ġwell Ġas Ġplain Ġand Ġpre process ed Ġdata . ĠFor Ġthe Ġmult ilingual ĠB ERT , Ġwe Ġemployed Ġthe Ġbase Ġmodel Ġin Ġboth Ġc ased Ġand Ġunc ased Ġvariations . ĠFor Ġthe ĠGerman ĠB ERT , Ġwe Ġutilized Ġthe Ġbase Ġmodel Ġin Ġthe Ġc ased Ġvariation Ġonly . ĠFor Ġboth Ġmodels , Ġwe Ġset Ġthe Ġlearning Ġrate Ġto Ġ$ 4 e - 5 $, Ġand Ġthe Ġmaximum Ġlength Ġof Ġthe Ġtoken ized Ġinput Ġwas Ġset Ġto Ġ128 Ġtokens . ĠWe Ġrun Ġthe Ġexperiments Ġmultiple Ġtimes Ġwith Ġdifferent Ġseeds Ġfor Ġa Ġmaximum Ġof Ġ300 Ġepoch s Ġwith Ġthe Ġtraining Ġbatch Ġsize Ġset Ġto Ġ32 . ĠWe Ġutilized ĠAdam W Ġas Ġthe Ġoptim izer Ġand Ġemployed Ġearly Ġstopping , Ġif Ġthe Ġperformance Ġdid Ġnot Ġchange Ġsignificantly Ġafter Ġ15 Ġepoch s . </s> E val uation Ġand ĠDiscussion : ĠFor Ġthe Ġevaluation , Ġwe Ġcomputed Ġword - level Ġmacro ĠF 1 Ġscore Ġfor Ġthe ĠN ER Ġtask Ġand Ġutter ance - level Ġmacro ĠF 1 Ġscore Ġfor Ġthe ĠN AP Ġtask . ĠThe Ġword - level ĠF 1 Ġis Ġestimated Ġas Ġthe Ġaverage Ġof Ġthe ĠF 1 Ġscores Ġper Ġclass , Ġeach Ġcomputed Ġfrom Ġall Ġwords Ġin Ġthe Ġevaluation Ġand Ġtest Ġsets . ĠThe Ġresults Ġfor Ġthe ĠN ER Ġtask Ġare Ġdepicted Ġin ĠTable ĠT AB REF 49 . ĠFor Ġutter ance - level ĠF 1 , Ġa Ġsingle Ġlabel Ġ( i . e ., Ġnext Ġaction ) Ġis Ġobtained Ġfor Ġthe Ġwhole Ġutter ance . ĠThe Ġresults Ġfor Ġthe ĠN AP Ġtask Ġare Ġpresented Ġin ĠTable ĠT AB REF 48 . ĠWe Ġadditionally Ġcomputed Ġaverage Ġdialogue Ġaccuracy Ġfor Ġthe Ġbest Ġperforming ĠN AP Ġmodels . ĠThis Ġscore Ġdenotes Ġhow Ġwell Ġthe Ġpredicted Ġnext Ġactions Ġmatch Ġthe Ġgold Ġnext Ġactions Ġand Ġthus Ġform Ġthe Ġdialogue Ġflow Ġwithin Ġeach Ġconversation . ĠThe Ġaverage Ġdialogue Ġaccuracy Ġwas Ġcomputed Ġfor Ġ50 Ġdialog ues Ġin Ġthe Ġevaluation Ġand Ġtest Ġsets Ġrespectively . ĠThe Ġresults Ġare Ġdisplayed Ġin ĠTable ĠT AB REF 50 . </s> The Ġobtained Ġresults Ġfor Ġthe ĠN ER Ġtask Ġrevealed Ġthat ĠGerman ĠB ERT Ġperformed Ġsignificantly Ġbetter Ġthan Ġthe Ġmult ilingual ĠB ERT Ġmodel . ĠThe Ġperformance Ġof Ġthe Ġcustom ĠN ER Ġunit Ġis Ġat Ġ$ 0 . 93 $ Ġmacro ĠF 1 Ġpoints Ġfor Ġall Ġpossible Ġnamed Ġentities Ġ( see ĠTable ĠT AB REF 49 ). ĠIn Ġcontrast , Ġfor Ġthe ĠN AP Ġtask , Ġthe Ġmult ilingual ĠB ERT Ġmodel Ġobtained Ġbetter Ġperformance Ġthan Ġthe ĠGerman ĠB ERT Ġmodel . ĠHere , Ġthe Ġbest Ġperforming Ġsystem Ġin Ġthe Ġdefault Ġsetting Ġachieved Ġa Ġmacro ĠF 1 Ġof Ġ$ 0 . 677 $ Ġpoints Ġfor Ġ14 Ġpossible Ġlabels , Ġwhereas Ġthe Ġmodel Ġin Ġthe Ġextended Ġsetting Ġperformed Ġbetter ĠâĢĵ Ġits Ġhighest Ġmacro ĠF 1 Ġscore Ġis Ġ$ 0 . 752 $ Ġfor Ġthe Ġsame Ġamount Ġof Ġlabels Ġ( see ĠTable ĠT AB REF 48 ). ĠConsidering Ġthe Ġdialogue Ġaccuracy , Ġthe Ġextended Ġsystem Ġtrained Ġwith Ġmult ilingual ĠB ERT Ġachieved Ġbetter Ġresults Ġthan Ġthe Ġdefault Ġone Ġwith Ġ$ 0 . 801 $ Ġaccuracy Ġpoints Ġcompared Ġto Ġ$ 0 . 7 24 $ Ġaccuracy Ġpoints Ġfor Ġthe Ġtest Ġset Ġ( see ĠTable ĠT AB REF 50 ). ĠThe Ġoverall Ġobservation Ġfor Ġthe ĠN AP Ġis Ġthat Ġthe Ġcapital ized Ġsetting Ġimproved Ġthe Ġperformance Ġof Ġthe Ġmodel , Ġwhereas Ġthe Ġinclusion Ġof Ġpunct uation Ġhas Ġnot Ġpositively Ġinfluenced Ġthe Ġresults . </s> Error ĠAnalysis : </s> After Ġthe Ġevaluation Ġstep , Ġwe Ġanalyzed Ġthe Ġcases , Ġwhere Ġthe Ġmodel Ġfailed Ġto Ġpredict Ġthe Ġcorrect Ġaction Ġor Ġlabeled Ġthe Ġnamed Ġentity Ġspan Ġerrone ously . ĠBelow Ġwe Ġdescribe Ġthe Ġmost Ġcommon Ġerrors Ġfor Ġboth Ġtasks . </s> Next ĠAction ĠPrediction : ĠOne Ġof Ġthe Ġmost Ġfrequent Ġerrors Ġin Ġthe Ġdefault Ġmodel Ġwas Ġthe Ġmismatch Ġbetween Ġtwo Ġconsecutive Ġactions ĠâĢĵ Ġnamely , Ġthe Ġaction ĠQuestion ĠNumber Ġand ĠSub topic . ĠThat Ġis Ġdue Ġto Ġthe Ġorder Ġof Ġthese Ġactions Ġin Ġthe Ġconvers ational Ġflow : ĠOcc urrence Ġof Ġboth Ġactions Ġin Ġthe Ġdialogue Ġis Ġnot Ġstrict Ġand Ġsubstantially Ġdepends Ġon Ġthe Ġprevious Ġsystem Ġaction . ĠHowever , Ġthe Ġanalysis Ġof Ġthe Ġextended Ġmodel Ġrevealed Ġthat Ġthe Ġintroduction Ġof Ġadditional Ġcontext Ġin Ġthe Ġform Ġof Ġthe Ġprevious Ġaction Ġimproved Ġthe Ġperformance Ġof Ġthe Ġsystem Ġin Ġthis Ġparticular Ġcase Ġby Ġabout Ġ$ 60 \ % $. </s> N amed ĠEntity ĠRecogn ition : ĠThe Ġfailing Ġcases Ġinclude Ġmism atches Ġbetween Ġthe Ġtags ĠâĢ ľ chapter âĢ Ŀ Ġand ĠâĢ ľ other âĢ Ŀ , Ġand Ġthe Ġtags ĠâĢ ľ question Ġnumber âĢ Ŀ Ġand ĠâĢ ľ other âĢ Ŀ . ĠThis Ġtype Ġof Ġerror Ġarose Ġdue Ġto Ġthe Ġimperfect ly Ġlabeled Ġspan Ġof Ġa Ġmulti - word Ġnamed Ġentity . ĠIn Ġsuch Ġcases , Ġthe Ġfirst Ġor Ġlast Ġword Ġin Ġthe Ġnamed Ġentity Ġwas Ġexcluded Ġfrom Ġthe Ġspan Ġand Ġerrone ously Ġlabeled Ġwith Ġthe Ġtag ĠâĢ ľ other âĢ Ŀ . </s> Related ĠWork </s> Individual Ġcomponents Ġof Ġa Ġparticular Ġdialogue Ġsystem Ġcould Ġbe Ġimplemented Ġusing Ġa Ġdifferent Ġkind Ġof Ġapproach , Ġstarting Ġwith Ġentirely Ġrule - Ġand Ġtemplate - based Ġmethods , Ġand Ġgoing Ġtowards Ġhybrid Ġapproaches Ġ( using Ġlearn able Ġcomponents Ġalong Ġwith Ġhand crafted Ġunits ) Ġand Ġend - to - end Ġtrain able Ġmachine Ġlearning Ġmethods . </s> Rule - based ĠAppro aches : ĠThough Ġmany Ġof Ġthe Ġlatest Ġresearch Ġapproaches Ġhandle ĠNL U Ġand ĠNL G Ġunits Ġby Ġusing Ġstatistical ĠN LP Ġmodels ĠB IB REF 5 , ĠB IB REF 6 , ĠB IB REF 7 , Ġmost Ġof Ġthe Ġindust ri ally Ġdeployed Ġdialogue Ġsystems Ġstill Ġuse Ġmanual Ġfeatures Ġor Ġhand crafted Ġrules Ġfor Ġthe Ġstate Ġand Ġaction Ġprediction , Ġintent Ġclassification , Ġand Ġslot Ġfilling Ġtasks ĠB IB REF 8 , ĠB IB REF 9 . ĠThe Ġrule - based Ġapproach Ġensures Ġrobust ness Ġand Ġstable Ġperformance Ġthat Ġis Ġcrucial Ġfor Ġindustrial Ġsystems Ġthat Ġinteract Ġwith Ġa Ġlarge Ġnumber Ġof Ġusers Ġsimultaneously . ĠHowever , Ġit Ġis Ġhighly Ġexpensive Ġand Ġtime - consuming Ġto Ġdeploy Ġa Ġreal Ġdialogue Ġsystem Ġbuilt Ġin Ġthis Ġmanner . ĠThe Ġmajor Ġdisadvantage Ġis Ġthat Ġthe Ġusage Ġof Ġhand crafted Ġsystems Ġis Ġrestricted Ġto Ġa Ġspecific Ġdomain , Ġand Ġpossible Ġdomain Ġadaptation Ġrequires Ġextensive Ġmanual Ġengineering . </s> End - to - End ĠLearning ĠAppro aches : ĠDue Ġto Ġthe Ġrecent Ġadvance Ġof Ġend - to - end Ġneural Ġgener ative Ġmodels ĠB IB REF 10 , Ġmany Ġefforts Ġhave Ġbeen Ġmade Ġto Ġbuild Ġan Ġend - to - end Ġtrain able Ġarchitecture Ġfor Ġdialogue Ġsystems . ĠRather Ġthan Ġusing Ġthe Ġtraditional Ġpipeline , Ġan Ġend - to - end Ġmodel Ġis Ġconceived Ġas Ġa Ġsingle Ġmodule ĠB IB REF 8 . ĠDespite Ġhaving Ġbetter Ġadapt ability Ġcompared Ġto Ġany Ġrule - based Ġsystem Ġand Ġbeing Ġeasy Ġto Ġtrain , Ġend - to - end Ġapproaches Ġremain Ġunatt ain able Ġfor Ġcommercial Ġconvers ational Ġagents Ġoperating Ġon Ġreal - world Ġdata . ĠA Ġwell Ġand Ġcarefully Ġconstructed Ġtask - oriented Ġdialogue Ġsystem Ġin Ġa Ġknown Ġdomain Ġusing Ġhand crafted Ġrules Ġand Ġpred efined Ġresponses , Ġstill Ġoutper forms Ġthe Ġend - to - end Ġsystems Ġdue Ġto Ġits Ġrobust ness ĠB IB REF 11 , ĠB IB REF 12 . </s> Hy brid ĠAppro aches : ĠThough Ġend - to - end Ġlearning Ġis Ġan Ġattractive Ġsolution Ġfor Ġdialogue Ġsystems , Ġcurrent Ġtechniques Ġare Ġdata - intensive Ġand Ġrequire Ġlarge Ġamounts Ġof Ġdialog ues Ġto Ġlearn Ġsimple Ġactions . ĠTo Ġovercome Ġthis Ġdifficulty , ĠB IB REF 13 Ġ( BI B REF 13 ) Ġintroduce ĠHybrid ĠCode ĠNetworks Ġ( HC Ns ), Ġwhich Ġis Ġan Ġensemble Ġof Ġretrieval Ġand Ġtrain able Ġunits . ĠThe Ġauthors Ġreport , Ġthat Ġcompared Ġto Ġexisting Ġend - to - end Ġmethods , Ġtheir Ġapproach Ġconsiderably Ġreduces Ġthe Ġamount Ġof Ġdata Ġrequired Ġfor Ġtraining ĠB IB REF 13 . ĠHybrid Ġmodels Ġappear Ġto Ġreplace Ġthe Ġestablished Ġrule - Ġand Ġtemplate - based Ġapproaches Ġwhich Ġare Ġcurrently Ġutilized Ġin Ġan Ġindustrial Ġsetting . </s> Con clusions </s> In Ġthis Ġwork , Ġwe Ġimplemented Ġa Ġdialogue Ġsystem Ġfor ĠIntelligent ĠProcess ĠAutom ation Ġpurposes Ġthat Ġsimultaneously Ġsolves Ġtwo Ġproblems : ĠFirst , Ġit Ġreduces Ġrepetitive Ġand Ġtime - consuming Ġactivities Ġand , Ġtherefore , Ġallows Ġworkers Ġof Ġthe Ġe - learning Ġplatform Ġto Ġfocus Ġon Ġsolely Ġmathematical Ġand Ġhence Ġmore Ġcognitive ly Ġdemanding Ġquestions . ĠSecond , Ġby Ġinteracting Ġwith Ġusers , Ġit Ġaug ments Ġthe Ġresources Ġwith Ġstructured Ġand Ġlabeled Ġtraining Ġdata Ġfor Ġfurther Ġpossible Ġimplementation Ġof Ġlearn able Ġdialogue Ġcomponents . ĠThe Ġrealization Ġof Ġsuch Ġa Ġsystem Ġwas Ġconnected Ġwith Ġmany Ġchallenges . ĠAmong Ġothers Ġwere Ġmissing Ġstructured Ġdata , Ġambiguous Ġor Ġerroneous Ġuser - generated Ġtext Ġand Ġthe Ġnecessity Ġto Ġdeal Ġwith Ġalready Ġexisting Ġcorporate Ġtools Ġand Ġtheir Ġdesign . ĠThe Ġintroduced Ġmodel Ġallowed Ġus Ġto Ġaccumulate Ġstructured Ġand Ġto Ġsome Ġextent Ġlabeled Ġdata Ġwithout Ġany Ġspecial Ġefforts Ġfrom Ġthe Ġhuman Ġ( i . e ., Ġtut ors ) Ġside Ġ( e . g ., Ġmanual Ġannotation Ġof Ġexisting Ġdialog ues , Ġchange Ġof Ġthe Ġconvers ational Ġstructure ). ĠOnce Ġwe Ġcollected Ġstructured Ġdialog ues , Ġwe Ġwere Ġable Ġto Ġre - train Ġspecific Ġcomponents Ġof Ġthe Ġsystem Ġwith Ġdeep Ġlearning Ġmethods Ġand Ġachieved Ġreasonable Ġperformance Ġfor Ġall Ġproposed Ġtasks . </s> We Ġbelieve Ġthe Ġobtained Ġresults Ġare Ġrather Ġgood , Ġconsidering Ġa Ġrelatively Ġsmall Ġamount Ġof Ġdata Ġwe Ġutilized Ġto Ġfine - t une Ġthe Ġpre - trained Ġmodel . ĠWe , Ġtherefore , Ġconclude Ġthat Ġboth ĠNext ĠAction ĠPrediction Ġand ĠNamed ĠEntity ĠRecogn ition Ġcomponents Ġcould Ġbe Ġemployed Ġto Ġsubstitute Ġor Ġextend Ġthe Ġexisting Ġrule - based Ġmodules . ĠRule - based Ġunits Ġare Ġrestricted Ġin Ġtheir Ġcapabilities Ġand Ġcould Ġbe Ġhardly Ġadapt able Ġto Ġnovel Ġpatterns , Ġwhereas Ġthe Ġtrain able Ġunits Ġgeneral ize Ġbetter , Ġwhich Ġwe Ġbelieve Ġcould Ġreduce Ġthe Ġnumber Ġof Ġerroneous Ġpredictions Ġin Ġcase Ġof Ġunexpected Ġdialogue Ġbehavior . ĠFurthermore , Ġto Ġincrease Ġthe Ġoverall Ġrobust ness , Ġboth Ġrule - based Ġand Ġtrain able Ġcomponents Ġcould Ġbe Ġused Ġsynchron ously Ġas Ġa Ġhybrid Ġmodel : Ġin Ġcase Ġwhen Ġone Ġsystem Ġfails , Ġthe Ġdialogue Ġproceeds Ġon Ġthe Ġprediction Ġobtained Ġfrom Ġthe Ġother Ġmodel . </s> Future ĠWork </s> The Ġcore Ġof Ġthe Ġrule - based Ġmodel Ġis Ġa Ġdialogue Ġmanager Ġthat Ġdetermines Ġthe Ġcurrent Ġstate Ġof Ġthe Ġconversation Ġand Ġthe Ġpossible Ġnext Ġaction . ĠRule - based Ġsystems Ġare Ġgenerally Ġconsidered Ġto Ġbe Ġhardly Ġadapt able Ġto Ġnew Ġdomains ; Ġhowever , Ġour Ġdialogue Ġmanager Ġproved Ġto Ġbe Ġflexible Ġto Ġslight Ġmodifications Ġin Ġa Ġworkflow . ĠOne Ġof Ġthe Ġpossible Ġdirections Ġof Ġfuture Ġwork Ġwould Ġbe Ġthe Ġinvestigation Ġof Ġthe Ġgeneral Ġadapt ability Ġof Ġthe Ġdialogue Ġmanager Ġcore Ġto Ġother Ġscenarios Ġand Ġdomains Ġ( e . g ., Ġdifferent Ġcourse ). ĠFurther Ġinvestigation Ġcould Ġbe Ġtowards Ġthe Ġmulti - language Ġmod ality Ġfor Ġthe Ġre - im ple mented Ġunits . ĠSince Ġthe ĠO MB + Ġplatform Ġalso Ġsupports ĠEnglish Ġand ĠChinese , Ġit Ġwould Ġbe Ġinteresting Ġto Ġexamine Ġwhether Ġthe Ġsimple Ġtranslation Ġfrom Ġtarget Ġlanguage Ġ( i . e ., ĠEnglish , ĠChinese ) Ġto Ġsource Ġlanguage Ġ( i . e ., ĠGerman ) Ġwould Ġbe Ġsufficient Ġto Ġemploy Ġalready - assembled Ġdataset Ġand Ġpre - trained Ġunits . </s> Future ĠWork Ġ: :: ĠA cknowled gments . </s> We Ġgrate fully Ġacknowledge Ġthe ĠO MB + Ġteam Ġfor Ġthe Ġcollaboration Ġand Ġespecially Ġthank ĠR ued i ĠSe iler Ġand ĠMichael ĠHe im ann Ġfor Ġtheir Ġhelpful Ġfeedback Ġand Ġtechnical Ġsupport . ĠWe Ġare Ġindebted Ġto Ġthe Ġtut ors Ġfor Ġthe Ġevaluation Ġof Ġthe Ġsystem , Ġas Ġwell Ġas Ġto Ġthe Ġanonymous Ġreviewers Ġfor Ġtheir Ġvaluable Ġcomments . </s> OM B + ĠDesign </s> Figure ĠFIG REF 52 Ġpresents Ġan Ġexample Ġof Ġthe ĠO MB + ĠOnline ĠLearning ĠPlatform . </s> Mut ually ĠExclusive ĠRules </s> Ass ume Ġa Ġlist Ġof Ġall Ġtheoretically Ġpossible Ġdialogue Ġstates : Ġ$ S $ Ġ= Ġ[ topic , Ġsub - topic , Ġtraining , Ġexercise , Ġchapter Ġlevel , Ġsection Ġlevel , Ġquiz , Ġfinal Ġexamination , Ġquestion Ġnumber ] Ġand Ġfor Ġeach Ġelement Ġ$ s _{ n } $ Ġin Ġ$ S $ Ġis Ġtrue Ġthat : </s> This Ġwould Ġgive Ġus Ġall Ġgeneral Ġ( resp . Ġpossible ) Ġdialogue Ġstates Ġwithout Ġreference Ġto Ġthe Ġdesign Ġof Ġthe ĠO MB + Ġplatform . ĠHowever , Ġto Ġmake Ġthe Ġdialogue Ġstates Ġfully Ġsuitable Ġfor Ġthe ĠO MB +, Ġfrom Ġthe Ġgeneral Ġstates , Ġwe Ġtake Ġonly Ġthose , Ġwhich Ġare Ġvalid . ĠTo Ġdefine Ġthe Ġvalid ness Ġof Ġthe Ġstate , Ġwe Ġspecify Ġthe Ġfollowing Ġfive ĠMut ually ĠExclusive ĠRules Ġ( MER ): </s> Rule Ġ($ R _{ 1 } $ ) Ġin ĠTable ĠT AB REF 54 Ġdenotes Ġad missible Ġconfigurations Ġfor Ġtopic Ġand Ġmeans Ġthat Ġwe Ġare Ġeither Ġgiven Ġa Ġtopic Ġ($ T $ ) Ġor Ġnot Ġ($ \ l not ĠT $ ). </s> Rule Ġ($ R _{ 2 } $ ) Ġin ĠTable ĠT AB REF 55 Ġdenotes Ġthat Ġeither Ġno Ġinformation Ġon Ġthe Ġexamination Ġmode Ġis Ġgiven , Ġor Ġexamination Ġmode Ġis ĠTraining Ġ($ TR $ ) Ġor ĠExercise Ġ($ E $ ) Ġor ĠQu iz Ġ($ Q $ ) Ġor ĠFinal ĠExamination Ġ($ FE $ ), Ġbut Ġnot Ġmore Ġthan Ġone Ġmode Ġat Ġthe Ġsame Ġtime . </s> Rule Ġ($ R _{ 3 } $ ) Ġin ĠTable ĠT AB REF 56 Ġindicates Ġthat Ġeither Ġno Ġlevel Ġinformation Ġis Ġprovided , Ġor Ġthe Ġlevel Ġcorresponds Ġto Ġchapter Ġlevel Ġ($ CL $ ) Ġor Ġto Ġsection Ġlevel Ġ($ SL $ ), Ġbut Ġnot Ġto Ġboth Ġat Ġthe Ġsame Ġtime . </s> Rule Ġ($ R _{ 4 } $ ) Ġin ĠTable ĠT AB REF 57 Ġmeans Ġthat ĠTraining Ġ($ TR $ ) Ġand ĠExercise Ġ($ E $ ) Ġexamination Ġmodes Ġcan Ġeither Ġbelong Ġto Ġchapter Ġlevel Ġ($ CL $ ) Ġor Ġto Ġsection Ġlevel Ġ($ SL $ ), Ġbut Ġnot Ġto Ġboth Ġat Ġthe Ġsame Ġtime . </s> Rule Ġ($ R _{ 5 } $ ) Ġin ĠTable ĠT AB REF 58 Ġsymbol izes Ġthat Ġwe Ġcould Ġbe Ġeither Ġgiven Ġonly Ġa Ġtopic Ġ($ T $ ) Ġor Ġthe Ġcombination Ġof Ġtopic Ġand Ġsub - topic Ġ($ ST $ ) Ġat Ġthe Ġsame Ġtime , Ġor Ġonly Ġsub - topic , Ġor Ġno Ġinformation Ġon Ġthis Ġpoint Ġat Ġall . </s> We Ġthen Ġdefine Ġa Ġvalid Ġdialogue Ġstate , Ġas Ġa Ġdialogue Ġstate Ġthat Ġmeets Ġall Ġrequirements Ġof Ġthe Ġabove mentioned Ġrules : </s> After Ġwe Ġget Ġthe Ġvalid Ġstates Ġfor Ġour Ġdialog ues , Ġwe Ġwant Ġto Ġmake Ġa Ġmapping Ġfrom Ġeach Ġvalid Ġdialogue Ġstate Ġto Ġthe Ġnext Ġpossible Ġsystems Ġaction . ĠFor Ġthat , Ġwe Ġfirst Ġdefine Ġfive Ġtransition Ġrules Ġ: </s> me ans Ġthat Ġno Ġtopic Ġ($ T $ ) Ġis Ġfound Ġin Ġthe ĠID Ġ( i . e ., Ġcould Ġnot Ġbe Ġextracted Ġfrom Ġuser Ġinput ). </s> ind icates Ġthat Ġno Ġexamination Ġmode Ġ($ EM $ ) Ġis Ġfound Ġin Ġthe ĠID . </s> den otes Ġthat Ġthe Ġextracted Ġexamination Ġmode Ġ($ EM $ ) Ġis Ġeither ĠTraining Ġ($ TR $ ) Ġor ĠExercise Ġ($ E $ ). </s> me ans Ġthat Ġno Ġsub - topic Ġ($ ST $ ) Ġis Ġprovided Ġby Ġa Ġuser , Ġbut ĠID Ġeither Ġalready Ġcontains Ġthe Ġcombination Ġof Ġtopic Ġ($ T $ ), Ġtraining Ġ($ TR $ ) Ġand Ġsection Ġlevel Ġ($ SL $ ), Ġor Ġthe Ġcombination Ġof Ġtopic , Ġexercise Ġ($ E $ ) Ġand Ġsection Ġlevel , Ġor Ġthe Ġcombination Ġof Ġtopic Ġand Ġquiz Ġ($ Q $ ). </s> ind icates Ġthat Ġno Ġquestion Ġnumber Ġ($ Q NR $ ) Ġwas Ġprovided Ġby Ġa Ġstudent Ġ( or Ġcould Ġnot Ġbe Ġsuccessfully Ġextracted ). </s> Finally , Ġwe Ġassumed Ġthe Ġlist Ġof Ġpossible Ġnext Ġactions Ġfor Ġthe Ġsystem : </s> Following Ġthe Ġtransition Ġrules , Ġwe Ġmapped Ġeach Ġvalid Ġdialogue Ġstate Ġto Ġthe Ġpossible Ġnext Ġaction Ġ$ a _{ m } $ Ġin Ġ$ A $ : </s> in Ġthe Ġcase Ġwhere Ġwe Ġdo Ġnot Ġhave Ġany Ġtopic Ġprovided , Ġthe Ġnext Ġaction Ġis Ġto Ġask Ġfor Ġthe Ġtopic Ġ($ T $ ). </s> if Ġno Ġexamination Ġmode Ġis Ġprovided Ġby Ġa Ġuser Ġ( or Ġit Ġcould Ġnot Ġbe Ġsuccessfully Ġextracted Ġfrom Ġthe Ġuser Ġquery ), Ġthe Ġnext Ġaction Ġis Ġdefined Ġas Ġask Ġfor Ġexamination Ġmode Ġ($ EM $ ). </s> in Ġcase Ġwhere Ġwe Ġknow Ġthe Ġexamination Ġmode Ġ$ EM $ Ġ$\ in Ġ$ Ġ[ Training , ĠExercise ], Ġwe Ġhave Ġto Ġask Ġabout Ġthe Ġlevel Ġ( i . e ., Ġtraining Ġat Ġchapter Ġlevel Ġor Ġtraining Ġat Ġsection Ġlevel ), Ġthus Ġthe Ġnext Ġaction Ġis Ġask Ġfor Ġlevel Ġ($ L $ ). </s> if Ġno Ġsub - topic Ġis Ġprovided , Ġbut Ġthe Ġexamination Ġmode Ġ$ EM $ Ġ$\ in Ġ$ Ġ[ Training , ĠExercise ] Ġat Ġsection Ġlevel , Ġthe Ġnext Ġaction Ġis Ġdefined Ġas Ġask Ġfor Ġsub - topic Ġ($ ST $ ). </s> if Ġno Ġquestion Ġnumber Ġis Ġprovided Ġby Ġa Ġuser , Ġthen Ġthe Ġnext Ġaction Ġis Ġask Ġfor Ġquestion Ġnumber Ġ($ Q NR $ ). </s> Com ple teness Ġof ĠID : ĠExample ĠCases </s> Below Ġare Ġexamples Ġof Ġtwo Ġfinal Ġcases Ġ( out Ġof Ġsix ), Ġwhere ĠID Ġis Ġconsidered Ġto Ġbe Ġcomplete : </s> Inter action ĠShow cases </s> Below Ġare Ġfive Ġsample Ġdialog ues Ġwith Ġvariable Ġflows .