English
EXTRACTIVE
English
ABSTRACTIVE
The Wikipedia dataset consists of articles from English Wikipedia, with quality class labels assigned by the Wikipedia community. Wikipedia articles are labelled with one of six quality classes, in descending order of quality: Featured Article (“FA”), Good Article (“GA”), B-class Article (“B”), C-class Article (“C”), Start Article (“Start”), and Stub Article (“Stub”). A description of the criteria associated with the different classes can be found in the Wikipedia grading scheme page. The quality class of a Wikipedia article is assigned by Wikipedia reviewers or any registered user, who can discuss through the article's talk page to reach consensus. We constructed the dataset by first crawling all articles from each quality class repository, e.g., we get FA articles by crawling pages from the FA repository: https://en.wikipedia.org/wiki/Category:Featured_articles. This resulted in around 5K FA, 28K GA, 212K B, 533K C, 2.6M Start, and 3.2M Stub articles.
The Wikipedia dataset consists of articles from English Wikipedia, with quality class labels assigned by the Wikipedia community. Wikipedia articles are labelled with one of six quality classes, in descending order of quality: Featured Article (“FA”), Good Article (“GA”), B-class Article (“B”), C-class Article (“C”), Start Article (“Start”), and Stub Article (“Stub”). A description of the criteria associated with the different classes can be found in the Wikipedia grading scheme page. The quality class of a Wikipedia article is assigned by Wikipedia reviewers or any registered user, who can discuss through the article's talk page to reach consensus. We constructed the dataset by first crawling all articles from each quality class repository, e.g., we get FA articles by crawling pages from the FA repository: https://en.wikipedia.org/wiki/Category:Featured_articles. This resulted in around 5K FA, 28K GA, 212K B, 533K C, 2.6M Start, and 3.2M Stub articles. The arXiv dataset BIBREF2 consists of three subsets of academic articles under the arXiv repository of Computer Science (cs), from the three subject areas of: Artificial Intelligence (cs.ai), Computation and Language (cs.cl), and Machine Learning (cs.lg). In line with the original dataset formulation BIBREF2 , a paper is considered to have been accepted (i.e. is positively labeled) if it matches a paper in the DBLP database or is otherwise accepted by any of the following conferences: ACL, EMNLP, NAACL, EACL, TACL, NIPS, ICML, ICLR, or AAAI. Failing this, it is considered to be rejected (noting that some of the papers may not have been submitted to one of these conferences). The median numbers of pages for papers in cs.ai, cs.cl, and cs.lg are 11, 10, and 12, respectively. To make sure each page in the PDF file has the same size in the screenshot, we crop the PDF file of a paper to the first 12; we pad the PDF file with blank pages if a PDF file has less than 12 pages, using the PyPDF2 Python package. We then use ImageMagick to convert the 12-page PDF file to a single 1,000 $\times $ 2,000 pixel screenshot. Table 2 details this dataset, where the “Accepted” column denotes the percentage of positive instances (accepted papers) in each subset.
1901.01010
Introduction </s> The Ġtask Ġof Ġdocument Ġquality Ġassessment Ġis Ġto Ġautomatically Ġassess Ġa Ġdocument Ġaccording Ġto Ġsome Ġpred efined Ġinventory Ġof Ġquality Ġlabels . ĠThis Ġcan Ġtake Ġmany Ġforms , Ġincluding Ġessay Ġscoring Ġ( quality Ġ= Ġlanguage Ġquality , Ġco herence , Ġand Ġrelevance Ġto Ġa Ġtopic ), Ġjob Ġapplication Ġfiltering Ġ( quality Ġ= Ġsuit ability Ġfor Ġrole Ġ+ Ġvisual / present ational Ġquality Ġof Ġthe Ġapplication ), Ġor Ġanswer Ġselection Ġin Ġcommunity Ġquestion Ġanswering Ġ( quality Ġ= Ġaction ability Ġ+ Ġrelevance Ġof Ġthe Ġanswer Ġto Ġthe Ġquestion ). ĠIn Ġthe Ġcase Ġof Ġthis Ġpaper , Ġwe Ġfocus Ġon Ġdocument Ġquality Ġassessment Ġin Ġtwo Ġcontexts : ĠWikipedia Ġdocument Ġquality Ġclassification , Ġand Ġwhether Ġa Ġpaper Ġsubmitted Ġto Ġa Ġconference Ġwas Ġaccepted Ġor Ġnot . </s> Aut omatic Ġquality Ġassessment Ġhas Ġobvious Ġbenefits Ġin Ġterms Ġof Ġtime Ġsavings Ġand Ġtract ability Ġin Ġcontexts Ġwhere Ġthe Ġvolume Ġof Ġdocuments Ġis Ġlarge . ĠIn Ġthe Ġcase Ġof Ġdynamic Ġdocuments Ġ( possibly Ġwith Ġmultiple Ġauthors ), Ġsuch Ġas Ġin Ġthe Ġcase Ġof ĠWikipedia , Ġit Ġis Ġparticularly Ġpertinent , Ġas Ġany Ġedit Ġpotentially Ġhas Ġimplications Ġfor Ġthe Ġquality Ġlabel Ġof Ġthat Ġdocument Ġ( and Ġaround Ġ10 ĠEnglish ĠWikipedia Ġdocuments Ġare Ġedited Ġper Ġsecond ). ĠFurthermore , Ġwhen Ġthe Ġquality Ġassessment Ġtask Ġis Ġdecentralized Ġ( as Ġin Ġthe Ġcase Ġof ĠWikipedia Ġand Ġacademic Ġpaper Ġassessment ), Ġquality Ġcriteria Ġare Ġoften Ġapplied Ġinconsist ently Ġby Ġdifferent Ġpeople , Ġwhere Ġan Ġautomatic Ġdocument Ġquality Ġassessment Ġsystem Ġcould Ġpotentially Ġreduce Ġinconsistencies Ġand Ġenable Ġimmediate Ġauthor Ġfeedback . </s> Current Ġstudies Ġon Ġdocument Ġquality Ġassessment Ġmainly Ġfocus Ġon Ġtextual Ġfeatures . ĠFor Ġexample , ĠB IB REF 0 Ġexamine Ġfeatures Ġsuch Ġas Ġthe Ġarticle Ġlength Ġand Ġthe Ġnumber Ġof Ġhead ings Ġto Ġpredict Ġthe Ġquality Ġclass Ġof Ġa ĠWikipedia Ġarticle . ĠIn Ġcontrast Ġto Ġthese Ġstudies , Ġin Ġthis Ġpaper , Ġwe Ġpropose Ġto Ġcombine Ġtext Ġfeatures Ġwith Ġvisual Ġfeatures , Ġbased Ġon Ġa Ġvisual Ġrendering Ġof Ġthe Ġdocument . ĠFigure Ġ1 Ġillustrates Ġour Ġintuition , Ġrelative Ġto ĠWikipedia Ġarticles . ĠWithout Ġbeing Ġable Ġto Ġread Ġthe Ġtext , Ġwe Ġcan Ġtell Ġthat Ġthe Ġarticle Ġin ĠFigure Ġ1 Ġhas Ġhigher Ġquality Ġthan ĠFigure Ġ1 Ġ, Ġas Ġit Ġhas Ġa Ġdetailed Ġinf ob ox , Ġextensive Ġreferences , Ġand Ġa Ġvariety Ġof Ġimages . ĠBased Ġon Ġthis Ġintuition , Ġwe Ġaim Ġto Ġanswer Ġthe Ġfollowing Ġquestion : Ġcan Ġwe Ġachieve Ġbetter Ġaccuracy Ġon Ġdocument Ġquality Ġassessment Ġby Ġcomplement ing Ġtextual Ġfeatures Ġwith Ġvisual Ġfeatures ? </s> Our Ġvisual Ġmodel Ġis Ġbased Ġon Ġfine - tun ing Ġan ĠIn ception ĠV 3 Ġmodel ĠB IB REF 1 Ġover Ġvisual Ġrender ings Ġof Ġdocuments , Ġwhile Ġour Ġtextual Ġmodel Ġis Ġbased Ġon Ġa Ġhierarchical Ġbi L ST M . ĠWe Ġfurther Ġcombine Ġthe Ġtwo Ġinto Ġa Ġjoint Ġmodel . ĠWe Ġperform Ġexperiments Ġon Ġtwo Ġdatasets : Ġa ĠWikipedia Ġdataset Ġnovel Ġto Ġthis Ġpaper , Ġand Ġan Ġar X iv Ġdataset Ġprovided Ġby ĠB IB REF 2 Ġsplit Ġinto Ġthree Ġsub - parts Ġbased Ġon Ġsubject Ġcategory . ĠExperimental Ġresults Ġon Ġthe Ġvisual Ġrender ings Ġof Ġdocuments Ġshow Ġthat Ġimplicit Ġquality Ġindicators , Ġsuch Ġas Ġimages Ġand Ġvisual Ġlayout , Ġcan Ġbe Ġcaptured Ġby Ġan Ġimage Ġclass ifier , Ġat Ġa Ġlevel Ġcomparable Ġto Ġa Ġtext Ġclass ifier . ĠWhen Ġwe Ġcombine Ġthe Ġtwo Ġmodels , Ġwe Ġachieve Ġstate - of - the - art Ġresults Ġover Ġ3 / 4 Ġof Ġour Ġdatasets . </s> This Ġpaper Ġmakes Ġthe Ġfollowing Ġcontributions : </s> All Ġcode Ġand Ġdata Ġassociated Ġwith Ġthis Ġresearch Ġwill Ġbe Ġreleased Ġon Ġpublication . </s> Related ĠWork </s> A Ġvariety Ġof Ġapproaches Ġhave Ġbeen Ġproposed Ġfor Ġdocument Ġquality Ġassessment Ġacross Ġdifferent Ġdomains : ĠWikipedia Ġarticle Ġquality Ġassessment , Ġacademic Ġpaper Ġrating , Ġcontent Ġquality Ġassessment Ġin Ġcommunity Ġquestion Ġanswering Ġ( c Q A ), Ġand Ġessay Ġscoring . ĠAmong Ġthese Ġapproaches , Ġsome Ġuse Ġhand - crafted Ġfeatures Ġwhile Ġothers Ġuse Ġneural Ġnetworks Ġto Ġlearn Ġfeatures Ġfrom Ġdocuments . ĠFor Ġeach Ġdomain , Ġwe Ġfirst Ġbriefly Ġdescribe Ġfeature - based Ġapproaches Ġand Ġthen Ġreview Ġneural Ġnetwork - based Ġapproaches . ĠWikipedia Ġarticle Ġquality Ġassessment : ĠQuality Ġassessment Ġof ĠWikipedia Ġarticles Ġis Ġa Ġtask Ġthat Ġassigns Ġa Ġquality Ġclass Ġlabel Ġto Ġa Ġgiven ĠWikipedia Ġarticle , Ġmirror ing Ġthe Ġquality Ġassessment Ġprocess Ġthat Ġthe ĠWikipedia Ġcommunity Ġcarries Ġout Ġmanually . ĠMany Ġapproaches Ġhave Ġbeen Ġproposed Ġthat Ġuse Ġfeatures Ġfrom Ġthe Ġarticle Ġitself , Ġmeta - data Ġfeatures Ġ( e . g ., Ġthe Ġeditors , Ġand ĠWikipedia Ġarticle Ġrevision Ġhistory ), Ġor Ġa Ġcombination Ġof Ġthe Ġtwo . ĠArticle - internal Ġfeatures Ġcapture Ġinformation Ġsuch Ġas Ġwhether Ġan Ġarticle Ġis Ġproperly Ġorganized , Ġwith Ġsupporting Ġevidence , Ġand Ġwith Ġappropriate Ġterminology . ĠFor Ġexample , ĠB IB REF 3 Ġuse Ġwriting Ġstyles Ġrepresented Ġby Ġbin ar ized Ġcharacter Ġtrig ram Ġfeatures Ġto Ġidentify Ġfeatured Ġarticles . ĠB IB REF 4 Ġand ĠB IB REF 0 Ġexplore Ġthe Ġnumber Ġof Ġhead ings , Ġimages , Ġand Ġreferences Ġin Ġthe Ġarticle . ĠB IB REF 5 Ġuse Ġnine Ġread ability Ġscores , Ġsuch Ġas Ġthe Ġpercentage Ġof Ġdifficult Ġwords Ġin Ġthe Ġdocument , Ġto Ġmeasure Ġthe Ġquality Ġof Ġthe Ġarticle . ĠMeta - data Ġfeatures , Ġwhich Ġare Ġindirect Ġindicators Ġof Ġarticle Ġquality , Ġare Ġusually Ġextracted Ġfrom Ġrevision Ġhistory , Ġand Ġthe Ġinteraction Ġbetween Ġeditors Ġand Ġarticles . ĠFor Ġexample , Ġone Ġhe uristic Ġthat Ġhas Ġbeen Ġproposed Ġis Ġthat Ġhigher - quality Ġarticles Ġhave Ġmore Ġedits ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ. ĠB IB REF 8 Ġuse Ġthe Ġpercentage Ġof Ġregistered Ġeditors Ġand Ġthe Ġtotal Ġnumber Ġof Ġeditors Ġof Ġan Ġarticle . ĠArticle âĢĵ editor Ġdependencies Ġhave Ġalso Ġbeen Ġexplored . ĠFor Ġexample , ĠB IB REF 9 Ġuse Ġthe Ġauthority Ġof Ġeditors Ġto Ġmeasure Ġthe Ġquality Ġof ĠWikipedia Ġarticles , Ġwhere Ġthe Ġauthority Ġof Ġeditors Ġis Ġdetermined Ġby Ġthe Ġarticles Ġthey Ġedit . ĠDeep Ġlearning Ġapproaches Ġto Ġpredicting ĠWikipedia Ġarticle Ġquality Ġhave Ġalso Ġbeen Ġproposed . ĠFor Ġexample , ĠB IB REF 10 Ġuse Ġa Ġversion Ġof Ġdoc 2 vec ĠB IB REF 11 Ġto Ġrepresent Ġarticles , Ġand Ġfeed Ġthe Ġdocument Ġembed d ings Ġinto Ġa Ġfour Ġhidden Ġlayer Ġneural Ġnetwork . ĠB IB REF 12 Ġfirst Ġobtain Ġsentence Ġrepresentations Ġby Ġaveraging Ġwords Ġwithin Ġa Ġsentence , Ġand Ġthen Ġapply Ġa Ġbi L ST M ĠB IB REF 13 Ġto Ġlearn Ġa Ġdocument - level Ġrepresentation , Ġwhich Ġis Ġcombined Ġwith Ġhand - crafted Ġfeatures Ġas Ġside Ġinformation . ĠB IB REF 14 Ġexploit Ġtwo Ġstacked Ġbi L ST Ms Ġto Ġlearn Ġdocument Ġrepresentations . </s> Ac ademic Ġpaper Ġrating : ĠAcademic Ġpaper Ġrating Ġis Ġa Ġrelatively Ġnew Ġtask Ġin ĠN LP / AI , Ġwith Ġthe Ġbasic Ġformulation Ġbeing Ġto Ġautomatically Ġpredict Ġwhether Ġto Ġaccept Ġor Ġreject Ġa Ġpaper . ĠB IB REF 2 Ġexplore Ġhand - crafted Ġfeatures , Ġsuch Ġas Ġthe Ġlength Ġof Ġthe Ġtitle , Ġwhether Ġspecific Ġwords Ġ( such Ġas Ġoutper form , Ġstate - of - the - art , Ġand Ġnovel ) Ġappear Ġin Ġthe Ġabstract , Ġand Ġan Ġembedded Ġrepresentation Ġof Ġthe Ġabstract Ġas Ġinput Ġto Ġdifferent Ġdownstream Ġlearners , Ġsuch Ġas Ġlog istic Ġregression , Ġdecision Ġtree , Ġand Ġrandom Ġforest . ĠB IB REF 15 Ġexploit Ġa Ġmodular ized Ġhierarchical Ġconv olution al Ġneural Ġnetwork Ġ( CNN ), Ġwhere Ġeach Ġpaper Ġsection Ġis Ġtreated Ġas Ġa Ġmodule . ĠFor Ġeach Ġpaper Ġsection , Ġthey Ġtrain Ġan Ġattention - based ĠCNN , Ġand Ġan Ġattentive Ġpool ing Ġlayer Ġis Ġapplied Ġto Ġthe Ġconc aten ated Ġrepresentation Ġof Ġeach Ġsection , Ġwhich Ġis Ġthen Ġfed Ġinto Ġa Ġsoft max Ġlayer . </s> Content Ġquality Ġassessment Ġin Ġc Q A : ĠAutomatic Ġquality Ġassessment Ġin Ġc Q A Ġis Ġthe Ġtask Ġof Ġdetermining Ġwhether Ġan Ġanswer Ġis Ġof Ġhigh Ġquality , Ġselected Ġas Ġthe Ġbest Ġanswer , Ġor Ġranked Ġhigher Ġthan Ġother Ġanswers . ĠTo Ġmeasure Ġanswer Ġcontent Ġquality Ġin Ġc Q A , Ġresearchers Ġhave Ġexploited Ġvarious Ġfeatures Ġfrom Ġdifferent Ġsources , Ġsuch Ġas Ġthe Ġanswer Ġcontent Ġitself , Ġthe Ġansw erer 's Ġprofile , Ġinteractions Ġamong Ġusers , Ġand Ġusage Ġof Ġthe Ġcontent . ĠThe Ġmost Ġcommon Ġfeature Ġused Ġis Ġthe Ġanswer Ġlength ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, Ġwith Ġother Ġfeatures Ġincluding : Ġsynt actic Ġand Ġsemantic Ġfeatures , Ġsuch Ġas Ġread ability Ġscores . ĠB IB REF 18 Ġ; Ġsimilarity Ġbetween Ġthe Ġquestion Ġand Ġthe Ġanswer Ġat Ġlex ical , Ġsynt actic , Ġand Ġsemantic Ġlevels ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ; Ġor Ġuser Ġdata Ġ( e . g ., Ġa Ġuser 's Ġstatus Ġpoints Ġor Ġthe Ġnumber Ġof Ġanswers Ġwritten Ġby Ġthe Ġuser ). ĠThere Ġhave Ġalso Ġbeen Ġapproaches Ġusing Ġneural Ġnetworks . ĠFor Ġexample , ĠB IB REF 21 Ġcombine ĠCNN - learn ed Ġrepresentations Ġwith Ġhand - crafted Ġfeatures Ġto Ġpredict Ġanswer Ġquality . ĠB IB REF 22 Ġuse Ġa Ġ2 - dimensional ĠCNN Ġto Ġlearn Ġthe Ġsemantic Ġrelevance Ġof Ġan Ġanswer Ġto Ġthe Ġquestion , Ġand Ġapply Ġan ĠL ST M Ġto Ġthe Ġanswer Ġsequence Ġto Ġmodel Ġthread Ġcontext . ĠB IB REF 23 Ġand ĠB IB REF 24 Ġmodel Ġthe Ġproblem Ġsimilarly Ġto Ġmachine Ġtranslation Ġquality Ġestimation , Ġtreating Ġanswers Ġas Ġcompeting Ġtranslation Ġhypotheses Ġand Ġthe Ġquestion Ġas Ġthe Ġreference Ġtranslation , Ġand Ġapply Ġneural Ġmachine Ġtranslation Ġto Ġthe Ġproblem . ĠEss ay Ġscoring : ĠAutom ated Ġessay Ġscoring Ġis Ġthe Ġtask Ġof Ġassigning Ġa Ġscore Ġto Ġan Ġessay , Ġusually Ġin Ġthe Ġcontext Ġof Ġassessing Ġthe Ġlanguage Ġability Ġof Ġa Ġlanguage Ġlear ner . ĠThe Ġquality Ġof Ġan Ġessay Ġis Ġaffected Ġby Ġthe Ġfollowing Ġfour Ġprimary Ġdimensions : Ġtopic Ġrelevance , Ġorganization Ġand Ġco herence , Ġword Ġusage Ġand Ġsentence Ġcomplexity , Ġand Ġgrammar Ġand Ġmechanics . ĠTo Ġmeasure Ġwhether Ġan Ġessay Ġis Ġrelevant Ġto Ġits ĠâĢ ľ prom pt âĢ Ŀ Ġ( the Ġdescription Ġof Ġthe Ġessay Ġtopic ), Ġlex ical Ġand Ġsemantic Ġoverlap Ġis Ġcommonly Ġused ĠB IB REF 25 Ġ, ĠB IB REF 26 Ġ. ĠB IB REF 27 Ġexplore Ġword Ġfeatures , Ġsuch Ġas Ġthe Ġnumber Ġof Ġverb Ġformation Ġerrors , Ġaverage Ġword Ġfrequency , Ġand Ġaverage Ġword Ġlength , Ġto Ġmeasure Ġword Ġusage Ġand Ġlex ical Ġcomplexity . ĠB IB REF 28 Ġuse Ġsentence Ġstructure Ġfeatures Ġto Ġmeasure Ġsentence Ġvariety . ĠThe Ġeffects Ġof Ġgram matical Ġand Ġmechanic Ġerrors Ġon Ġthe Ġquality Ġof Ġan Ġessay Ġare Ġmeasured Ġvia Ġword Ġand Ġpart - of - speech Ġ$ n $ Ġ- gram Ġfeatures Ġand ĠâĢ ľ me chan ics âĢ Ŀ Ġfeatures ĠB IB REF 29 Ġ( e . g ., Ġspelling , Ġcapital ization , Ġand Ġpunct uation ), Ġrespectively . ĠB IB REF 30 Ġ, ĠB IB REF 31 Ġ, Ġand ĠB IB REF 32 Ġuse Ġan ĠL ST M Ġto Ġobtain Ġan Ġessay Ġrepresentation , Ġwhich Ġis Ġused Ġas Ġthe Ġbasis Ġfor Ġclassification . ĠSimilarly , ĠB IB REF 33 Ġutilize Ġa ĠCNN Ġto Ġobtain Ġsentence Ġrepresentation Ġand Ġan ĠL ST M Ġto Ġobtain Ġessay Ġrepresentation , Ġwith Ġan Ġattention Ġlayer Ġat Ġboth Ġthe Ġsentence Ġand Ġessay Ġlevels . </s> The ĠProp osed ĠJoint ĠModel </s> We Ġtreat Ġdocument Ġquality Ġassessment Ġas Ġa Ġclassification Ġproblem , Ġi . e ., Ġgiven Ġa Ġdocument , Ġwe Ġpredict Ġits Ġquality Ġclass Ġ( e . g ., Ġwhether Ġan Ġacademic Ġpaper Ġshould Ġbe Ġaccepted Ġor Ġrejected ). ĠThe Ġproposed Ġmodel Ġis Ġa Ġjoint Ġmodel Ġthat Ġintegrates Ġvisual Ġfeatures Ġlearned Ġthrough ĠIn ception ĠV 3 Ġwith Ġtextual Ġfeatures Ġlearned Ġthrough Ġa Ġbi L ST M . ĠIn Ġthis Ġsection , Ġwe Ġpresent Ġthe Ġdetails Ġof Ġthe Ġvisual Ġand Ġtextual Ġembed d ings , Ġand Ġfinally Ġdescribe Ġhow Ġwe Ġcombine Ġthe Ġtwo . ĠWe Ġreturn Ġto Ġdiscuss Ġhyper - param eter Ġsettings Ġand Ġthe Ġexperimental Ġconfiguration Ġin Ġthe ĠExper iments Ġsection . </s> Visual ĠEmb edd ing ĠLearning </s> A Ġwide Ġrange Ġof Ġmodels Ġhave Ġbeen Ġproposed Ġto Ġtackle Ġthe Ġimage Ġclassification Ġtask , Ġsuch Ġas ĠV GG ĠB IB REF 34 Ġ, ĠRes Net ĠB IB REF 35 Ġ, ĠIn ception ĠV 3 ĠB IB REF 1 Ġ, Ġand ĠX ception ĠB IB REF 36 Ġ. ĠHowever , Ġto Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthere Ġis Ġno Ġexisting Ġwork Ġthat Ġhas Ġproposed Ġto Ġuse Ġvisual Ġrender ings Ġof Ġdocuments Ġto Ġassess Ġdocument Ġquality . ĠIn Ġthis Ġpaper , Ġwe Ġuse ĠIn ception ĠV 3 Ġpret rained Ġon ĠImage Net Ġ( âĢ ľ In ception âĢ Ŀ Ġhereafter ) Ġto Ġobtain Ġvisual Ġembed d ings Ġof Ġdocuments , Ġnoting Ġthat Ġany Ġimage Ġclass ifier Ġcould Ġbe Ġapplied Ġto Ġour Ġtask . ĠThe Ġinput Ġto ĠIn ception Ġis Ġa Ġvisual Ġrendering Ġ( sc reenshot ) Ġof Ġa Ġdocument , Ġand Ġthe Ġoutput Ġis Ġa Ġvisual Ġembed ding , Ġwhich Ġwe Ġwill Ġlater Ġintegrate Ġwith Ġour Ġtextual Ġembed ding . </s> Based Ġon Ġthe Ġobservation Ġthat Ġit Ġis Ġdifficult Ġto Ġdecide Ġwhat Ġtypes Ġof Ġconv olution Ġto Ġapply Ġto Ġeach Ġlayer Ġ( such Ġas Ġ3 Ġ$\ times Ġ$ Ġ3 Ġor Ġ5 Ġ$\ times Ġ$ Ġ5 ), Ġthe Ġbasic ĠIn ception Ġmodel Ġapplies Ġmultiple Ġconv olution Ġfilters Ġin Ġparallel Ġand Ġconc aten ates Ġthe Ġresulting Ġfeatures , Ġwhich Ġare Ġfed Ġinto Ġthe Ġnext Ġlayer . ĠThis Ġhas Ġthe Ġbenefit Ġof Ġcapturing Ġboth Ġlocal Ġfeatures Ġthrough Ġsmaller Ġconv olutions Ġand Ġabstract ed Ġfeatures Ġthrough Ġlarger Ġconv olutions . ĠIn ception Ġis Ġa Ġhybrid Ġof Ġmultiple ĠIn ception Ġmodels Ġof Ġdifferent Ġarchitectures . ĠTo Ġreduce Ġcomputational Ġcost , ĠIn ception Ġalso Ġmod ifies Ġthe Ġbasic Ġmodel Ġby Ġapplying Ġa Ġ1 Ġ$\ times Ġ$ Ġ1 Ġconv olution Ġto Ġthe Ġinput Ġand Ġfactor izing Ġlarger Ġconv olutions Ġinto Ġsmaller Ġones . </s> Text ual ĠEmb edd ing ĠLearning </s> We Ġadopt Ġa Ġbi - direction al ĠL ST M Ġmodel Ġto Ġgenerate Ġtextual Ġembed d ings Ġfor Ġdocument Ġquality Ġassessment , Ġfollowing Ġthe Ġmethod Ġof ĠB IB REF 12 Ġ( âĢ ľ bi L ST M âĢ Ŀ Ġhereafter ). ĠThe Ġinput Ġto Ġbi L ST M Ġis Ġa Ġtextual Ġdocument , Ġand Ġthe Ġoutput Ġis Ġa Ġtextual Ġembed ding , Ġwhich Ġwill Ġlater Ġintegrate Ġwith Ġthe Ġvisual Ġembed ding . </s> For Ġbi L ST M , Ġeach Ġword Ġis Ġrepresented Ġas Ġa Ġword Ġembed ding ĠB IB REF 37 Ġ, Ġand Ġan Ġaverage - pool ing Ġlayer Ġis Ġapplied Ġto Ġthe Ġword Ġembed d ings Ġto Ġobtain Ġthe Ġsentence Ġembed ding , Ġwhich Ġis Ġfed Ġinto Ġa Ġbi - direction al ĠL ST M Ġto Ġgenerate Ġthe Ġdocument Ġembed ding Ġfrom Ġthe Ġsentence Ġembed d ings . ĠThen Ġa Ġmax - pool ing Ġlayer Ġis Ġapplied Ġto Ġselect Ġthe Ġmost Ġsalient Ġfeatures Ġfrom Ġthe Ġcomponent Ġsentences . </s> The ĠJoint ĠModel </s> The Ġproposed Ġjoint Ġmodel Ġ( âĢ ľ J oint âĢ Ŀ Ġhereafter ) Ġcombines Ġthe Ġvisual Ġand Ġtextual Ġembed d ings Ġ( output Ġof ĠIn ception Ġand Ġbi L ST M ) Ġvia Ġa Ġsimple Ġfeed - forward Ġlayer Ġand Ġsoft max Ġover Ġthe Ġdocument Ġlabel Ġset , Ġas Ġshown Ġin ĠFigure Ġ2 Ġ. ĠWe Ġoptimize Ġour Ġmodel Ġbased Ġon Ġcross - ent ropy Ġloss . </s> Exper iments </s> In Ġthis Ġsection , Ġwe Ġfirst Ġdescribe Ġthe Ġtwo Ġdatasets Ġused Ġin Ġour Ġexperiments : Ġ( 1 ) ĠWikipedia , Ġand Ġ( 2 ) Ġar X iv . ĠThen , Ġwe Ġreport Ġthe Ġexperimental Ġdetails Ġand Ġresults . </s> Dat as ets </s> The ĠWikipedia Ġdataset Ġconsists Ġof Ġarticles Ġfrom ĠEnglish ĠWikipedia , Ġwith Ġquality Ġclass Ġlabels Ġassigned Ġby Ġthe ĠWikipedia Ġcommunity . ĠWikipedia Ġarticles Ġare Ġlabelled Ġwith Ġone Ġof Ġsix Ġquality Ġclasses , Ġin Ġdescending Ġorder Ġof Ġquality : ĠFeatured ĠArticle Ġ( âĢ ľ FA âĢ Ŀ ), ĠGood ĠArticle Ġ( âĢ ľ GA âĢ Ŀ ), ĠB - class ĠArticle Ġ( âĢ ľ B âĢ Ŀ ), ĠC - class ĠArticle Ġ( âĢ ľ C âĢ Ŀ ), ĠStart ĠArticle Ġ( âĢ ľ Start âĢ Ŀ ), Ġand ĠStub ĠArticle Ġ( âĢ ľ St ub âĢ Ŀ ). ĠA Ġdescription Ġof Ġthe Ġcriteria Ġassociated Ġwith Ġthe Ġdifferent Ġclasses Ġcan Ġbe Ġfound Ġin Ġthe ĠWikipedia Ġgrading Ġscheme Ġpage . ĠThe Ġquality Ġclass Ġof Ġa ĠWikipedia Ġarticle Ġis Ġassigned Ġby ĠWikipedia Ġreviewers Ġor Ġany Ġregistered Ġuser , Ġwho Ġcan Ġdiscuss Ġthrough Ġthe Ġarticle 's Ġtalk Ġpage Ġto Ġreach Ġconsensus . ĠWe Ġconstructed Ġthe Ġdataset Ġby Ġfirst Ġcrawling Ġall Ġarticles Ġfrom Ġeach Ġquality Ġclass Ġrepository , Ġe . g ., Ġwe Ġget ĠFA Ġarticles Ġby Ġcrawling Ġpages Ġfrom Ġthe ĠFA Ġrepository : Ġhttps :// en . wikipedia . org / wiki / Category : Featured _ articles . ĠThis Ġresulted Ġin Ġaround Ġ5 K ĠFA , Ġ28 K ĠGA , Ġ212 K ĠB , Ġ5 33 K ĠC , Ġ2 . 6 M ĠStart , Ġand Ġ3 . 2 M ĠStub Ġarticles . </s> We Ġrandomly Ġsampled Ġ5 , 000 Ġarticles Ġfrom Ġeach Ġquality Ġclass Ġand Ġremoved Ġall Ġredirect Ġpages , Ġresulting Ġin Ġa Ġdataset Ġof Ġ29 , 794 Ġarticles . ĠAs Ġthe Ġwik ite xt Ġcontained Ġin Ġeach Ġdocument Ġcontains Ġmarkup Ġrelating Ġto Ġthe Ġdocument Ġcategory Ġsuch Ġas Ġ{ Featured ĠArticle } Ġor Ġ{ ge o - st ub }, Ġwhich Ġreveals Ġthe Ġlabel , Ġwe Ġremove Ġsuch Ġinformation . ĠWe Ġadditionally Ġrandomly Ġpartition ed Ġthis Ġdataset Ġinto Ġtraining , Ġdevelopment , Ġand Ġtest Ġsplits Ġbased Ġon Ġa Ġratio Ġof Ġ8 : 1 : 1 . ĠDetails Ġof Ġthe Ġdataset Ġare Ġsummarized Ġin ĠTable Ġ1 Ġ. </s> We Ġgenerate Ġa Ġvisual Ġrepresentation Ġof Ġeach Ġdocument Ġvia Ġa Ġ1 , 000 Ġ$\ times Ġ$ Ġ2 , 000 - pixel Ġscreenshot Ġof Ġthe Ġarticle Ġvia Ġa ĠPhantom JS Ġscript Ġover Ġthe Ġrendered Ġversion Ġof Ġthe Ġarticle , Ġensuring Ġthat Ġthe Ġscreenshot Ġand Ġwik ite xt Ġversions Ġof Ġthe Ġarticle Ġare Ġthe Ġsame Ġversion . ĠAny Ġdirect Ġindicators Ġof Ġdocument Ġquality Ġ( such Ġas Ġthe ĠFA Ġindicator , Ġwhich Ġis Ġa Ġbronze Ġstar Ġicon Ġin Ġthe Ġtop Ġright Ġcorner Ġof Ġthe Ġwebpage ) Ġare Ġremoved Ġfrom Ġthe Ġscreenshot . </s> The Ġar X iv Ġdataset ĠB IB REF 2 Ġconsists Ġof Ġthree Ġsubs ets Ġof Ġacademic Ġarticles Ġunder Ġthe Ġar X iv Ġrepository Ġof ĠComputer ĠScience Ġ( cs ), Ġfrom Ġthe Ġthree Ġsubject Ġareas Ġof : ĠArtificial ĠIntelligence Ġ( cs . ai ), ĠComput ation Ġand ĠLanguage Ġ( cs . cl ), Ġand ĠMachine ĠLearning Ġ( cs . l g ). ĠIn Ġline Ġwith Ġthe Ġoriginal Ġdataset Ġformulation ĠB IB REF 2 Ġ, Ġa Ġpaper Ġis Ġconsidered Ġto Ġhave Ġbeen Ġaccepted Ġ( i . e . Ġis Ġpositively Ġlabeled ) Ġif Ġit Ġmatches Ġa Ġpaper Ġin Ġthe ĠD BL P Ġdatabase Ġor Ġis Ġotherwise Ġaccepted Ġby Ġany Ġof Ġthe Ġfollowing Ġconferences : ĠACL , ĠEM N LP , ĠNA AC L , ĠE AC L , ĠT AC L , ĠNI PS , ĠIC ML , ĠI CL R , Ġor ĠAAA I . ĠF ailing Ġthis , Ġit Ġis Ġconsidered Ġto Ġbe Ġrejected Ġ( not ing Ġthat Ġsome Ġof Ġthe Ġpapers Ġmay Ġnot Ġhave Ġbeen Ġsubmitted Ġto Ġone Ġof Ġthese Ġconferences ). ĠThe Ġmedian Ġnumbers Ġof Ġpages Ġfor Ġpapers Ġin Ġcs . ai , Ġcs . cl , Ġand Ġcs . l g Ġare Ġ11 , Ġ10 , Ġand Ġ12 , Ġrespectively . ĠTo Ġmake Ġsure Ġeach Ġpage Ġin Ġthe ĠPDF Ġfile Ġhas Ġthe Ġsame Ġsize Ġin Ġthe Ġscreenshot , Ġwe Ġcrop Ġthe ĠPDF Ġfile Ġof Ġa Ġpaper Ġto Ġthe Ġfirst Ġ12 ; Ġwe Ġpad Ġthe ĠPDF Ġfile Ġwith Ġblank Ġpages Ġif Ġa ĠPDF Ġfile Ġhas Ġless Ġthan Ġ12 Ġpages , Ġusing Ġthe ĠPy PDF 2 ĠPython Ġpackage . ĠWe Ġthen Ġuse ĠImage Mag ick Ġto Ġconvert Ġthe Ġ12 - page ĠPDF Ġfile Ġto Ġa Ġsingle Ġ1 , 000 Ġ$\ times Ġ$ Ġ2 , 000 Ġpixel Ġscreenshot . ĠTable Ġ2 Ġdetails Ġthis Ġdataset , Ġwhere Ġthe ĠâĢ ľ Accept ed âĢ Ŀ Ġcolumn Ġdenotes Ġthe Ġpercentage Ġof Ġpositive Ġinstances Ġ( accept ed Ġpapers ) Ġin Ġeach Ġsubset . </s> Exper imental ĠSetting </s> As Ġdiscussed Ġabove , Ġour Ġmodel Ġhas Ġtwo Ġmain Ġcomponents ĠâĢĶ Ġbi L ST M Ġand ĠIn ception âĢĶ Ġwhich Ġgenerate Ġtextual Ġand Ġvisual Ġrepresentations , Ġrespectively . ĠFor Ġthe Ġbi L ST M Ġcomponent , Ġthe Ġdocuments Ġare Ġpre process ed Ġas Ġdescribed Ġin ĠB IB REF 12 Ġ, Ġwhere Ġan Ġarticle Ġis Ġdivided Ġinto Ġsentences Ġand Ġtoken ized Ġusing ĠNL T K ĠB IB REF 38 Ġ. ĠWords Ġappearing Ġmore Ġthan Ġ20 Ġtimes Ġare Ġretained Ġwhen Ġbuilding Ġthe Ġvocabulary . ĠAll Ġother Ġwords Ġare Ġreplaced Ġby Ġthe Ġspecial ĠUN K Ġtoken . ĠWe Ġuse Ġthe Ġpre - trained ĠGl o Ve ĠB IB REF 39 Ġ50 - dimensional Ġword Ġembed d ings Ġto Ġrepresent Ġwords . ĠFor Ġwords Ġnot Ġin ĠGl o Ve , Ġword Ġembed d ings Ġare Ġrandomly Ġinitialized Ġbased Ġon Ġsampling Ġfrom Ġa Ġuniform Ġdistribution Ġ$ U (- 1 , Ġ1 ) $ Ġ. ĠAll Ġword Ġembed d ings Ġare Ġupdated Ġin Ġthe Ġtraining Ġprocess . ĠWe Ġset Ġthe ĠL ST M Ġhidden Ġlayer Ġsize Ġto Ġ256 . ĠThe Ġconc aten ation Ġof Ġthe Ġforward Ġand Ġbackward ĠL ST Ms Ġthus Ġgives Ġus Ġ512 Ġdimensions Ġfor Ġthe Ġdocument Ġembed ding . ĠA Ġdrop out Ġlayer Ġis Ġapplied Ġat Ġthe Ġsentence Ġand Ġdocument Ġlevel , Ġrespectively , Ġwith Ġa Ġprobability Ġof Ġ0 . 5 . </s> For ĠIn ception , Ġwe Ġadopt Ġdata Ġaug mentation Ġtechniques Ġin Ġthe Ġtraining Ġwith Ġa ĠâĢ ľ ne arest âĢ Ŀ Ġfilling Ġmode , Ġa Ġzoom Ġrange Ġof Ġ0 . 1 , Ġa Ġwidth Ġshift Ġrange Ġof Ġ0 . 1 , Ġand Ġa Ġheight Ġshift Ġrange Ġof Ġ0 . 1 . ĠAs Ġthe Ġoriginal Ġscreenshots Ġhave Ġthe Ġsize Ġof Ġ1 , 000 Ġ$\ times Ġ2 $ Ġ, 000 Ġpixels , Ġthey Ġare Ġres ized Ġto Ġ500 Ġ$\ times Ġ$ Ġ500 Ġto Ġfeed Ġinto ĠIn ception , Ġwhere Ġthe Ġinput Ġshape Ġis Ġ( 500 , Ġ500 , Ġ3 ). ĠA Ġdrop out Ġlayer Ġis Ġapplied Ġwith Ġa Ġprobability Ġof Ġ0 . 5 . ĠThen , Ġa ĠGlobal Average Pool ing 2 D Ġlayer Ġis Ġapplied , Ġwhich Ġproduces Ġa Ġ2 , 048 Ġdimensional Ġrepresentation . </s> For Ġthe ĠJoint Ġmodel , Ġwe Ġget Ġa Ġrepresentation Ġof Ġ2 , 560 Ġdimensions Ġby Ġconc aten ating Ġthe Ġ512 Ġdimensional Ġrepresentation Ġfrom Ġthe Ġbi L ST M Ġwith Ġthe Ġ2 , 048 Ġdimensional Ġrepresentation Ġfrom ĠIn ception . ĠThe Ġdrop out Ġlayer Ġis Ġapplied Ġto Ġthe Ġtwo Ġcomponents Ġwith Ġa Ġprobability Ġof Ġ0 . 5 . ĠFor Ġbi L ST M , Ġwe Ġuse Ġa Ġmini - batch Ġsize Ġof Ġ128 Ġand Ġa Ġlearning Ġrate Ġof Ġ0 . 001 . ĠFor Ġboth ĠIn ception Ġand Ġjoint Ġmodel , Ġwe Ġuse Ġa Ġmini - batch Ġsize Ġof Ġ16 Ġand Ġa Ġlearning Ġrate Ġof Ġ0 . 0001 . ĠAll Ġhyper - param eters Ġwere Ġset Ġempir ically Ġover Ġthe Ġdevelopment Ġdata , Ġand Ġthe Ġmodels Ġwere Ġoptimized Ġusing Ġthe ĠAdam Ġoptim izer ĠB IB REF 40 Ġ. </s> In Ġthe Ġtraining Ġphase , Ġthe Ġweights Ġin ĠIn ception Ġare Ġinitialized Ġby Ġparameters Ġpret rained Ġon ĠImage Net , Ġand Ġthe Ġweights Ġin Ġbi L ST M Ġare Ġrandomly Ġinitialized Ġ( except Ġfor Ġthe Ġword Ġembed d ings ). ĠWe Ġtrain Ġeach Ġmodel Ġfor Ġ50 Ġepoch s . ĠHowever , Ġto Ġprevent Ġover fitting , Ġwe Ġadopt Ġearly Ġstopping , Ġwhere Ġwe Ġstop Ġtraining Ġthe Ġmodel Ġif Ġthe Ġperformance Ġon Ġthe Ġdevelopment Ġset Ġdoes Ġnot Ġimprove Ġfor Ġ20 Ġepoch s . ĠFor Ġevaluation , Ġwe Ġuse Ġ( micro -) acc uracy , Ġfollowing Ġprevious Ġstudies ĠB IB REF 5 Ġ, ĠB IB REF 2 Ġ. </s> Bas eline ĠAppro aches </s> We Ġcompare Ġour Ġmodels Ġagainst Ġthe Ġfollowing Ġfive Ġbas elines : </s> Major ity : Ġthe Ġmodel Ġlabels Ġall Ġtest Ġsamples Ġwith Ġthe Ġmajority Ġclass Ġof Ġthe Ġtraining Ġdata . </s> Bench mark : Ġa Ġbenchmark Ġmethod Ġfrom Ġthe Ġliterature . ĠIn Ġthe Ġcase Ġof ĠWikipedia , Ġthis Ġis ĠB IB REF 5 Ġ, Ġwho Ġuse Ġstructural Ġfeatures Ġand Ġread ability Ġscores Ġas Ġfeatures Ġto Ġbuild Ġa Ġrandom Ġforest Ġclass ifier ; Ġfor Ġar X iv , Ġthis Ġis ĠB IB REF 2 Ġ, Ġwho Ġuse Ġhand - crafted Ġfeatures , Ġsuch Ġas Ġthe Ġnumber Ġof Ġreferences Ġand ĠTF - ID F Ġweighted Ġbag - of - words Ġin Ġabstract , Ġto Ġbuild Ġa Ġclass ifier Ġbased Ġon Ġthe Ġbest Ġof Ġlog istic Ġregression , Ġmulti - layer Ġperception , Ġand ĠAda Boost . </s> Doc 2 V ec : Ġdoc 2 vec ĠB IB REF 11 Ġto Ġlearn Ġdocument Ġembed d ings Ġwith Ġa Ġdimension Ġof Ġ500 , Ġand Ġa Ġ4 - layer Ġfeed - forward Ġclassification Ġmodel Ġon Ġtop Ġof Ġthis , Ġwith Ġ2000 , Ġ1000 , Ġ500 , Ġand Ġ200 Ġdimensions , Ġrespectively . </s> bi L ST M : Ġfirst Ġderive Ġa Ġsentence Ġrepresentation Ġby Ġaveraging Ġacross Ġwords Ġin Ġa Ġsentence , Ġthen Ġfeed Ġthe Ġsentence Ġrepresentation Ġinto Ġa Ġbi L ST M Ġand Ġa Ġmax pool ing Ġlayer Ġover Ġoutput Ġsequence Ġto Ġlearn Ġa Ġdocument Ġlevel Ġrepresentation Ġwith Ġa Ġdimension Ġof Ġ512 , Ġwhich Ġis Ġused Ġto Ġpredict Ġdocument Ġquality . </s> In ception Ġ$ _{ \ text { fixed }} $ Ġ: Ġthe Ġfrozen ĠIn ception Ġmodel , Ġwhere Ġonly Ġparameters Ġin Ġthe Ġlast Ġlayer Ġare Ġfine - tun ed Ġduring Ġtraining . </s> The Ġhyper - param eters Ġof ĠBench mark , ĠDoc 2 V ec , Ġand Ġbi L ST M Ġare Ġbased Ġon Ġthe Ġcorresponding Ġpapers Ġexcept Ġthat : Ġ( 1 ) Ġwe Ġfine - t une Ġthe Ġfeed Ġforward Ġlayer Ġof ĠDoc 2 V ec Ġon Ġthe Ġdevelopment Ġset Ġand Ġtrain Ġthe Ġmodel Ġ300 Ġepoch s Ġon ĠWikipedia Ġand Ġ50 Ġepoch s Ġon Ġar X iv ; Ġ( 2 ) Ġwe Ġdo Ġnot Ġuse Ġhand - crafted Ġfeatures Ġfor Ġbi L ST M Ġas Ġwe Ġwant Ġthe Ġbas elines Ġto Ġbe Ġcomparable Ġto Ġour Ġmodels , Ġand Ġthe Ġmain Ġfocus Ġof Ġthis Ġpaper Ġis Ġnot Ġto Ġexplore Ġthe Ġeffects Ġof Ġhand - crafted Ġfeatures Ġ( e . g ., Ġsee ĠB IB REF 12 Ġ). </s> Exper imental ĠResults </s> Table Ġ3 Ġshows Ġthe Ġperformance Ġof Ġthe Ġdifferent Ġmodels Ġover Ġour Ġtwo Ġdatasets , Ġin Ġthe Ġform Ġof Ġthe Ġaverage Ġaccuracy Ġon Ġthe Ġtest Ġset Ġ( along Ġwith Ġthe Ġstandard Ġdeviation ) Ġover Ġ10 Ġruns , Ġwith Ġdifferent Ġrandom Ġinitial izations . </s> On ĠWikipedia , Ġwe Ġobserve Ġthat Ġthe Ġperformance Ġof Ġbi L ST M , ĠIn ception , Ġand ĠJoint Ġis Ġmuch Ġbetter Ġthan Ġthat Ġof Ġall Ġfour Ġbas elines . ĠIn ception Ġachieves Ġ2 . 9 % Ġhigher Ġaccuracy Ġthan Ġbi L ST M . ĠThe Ġperformance Ġof ĠJoint Ġachieves Ġan Ġaccuracy Ġof Ġ59 . 4 %, Ġwhich Ġis Ġ5 . 3 % Ġhigher Ġthan Ġusing Ġtextual Ġfeatures Ġalone Ġ( bi L ST M ) Ġand Ġ2 . 4 % Ġhigher Ġthan Ġusing Ġvisual Ġfeatures Ġalone Ġ( In ception ). ĠBased Ġon Ġa Ġone - tailed ĠWil co xon Ġsigned - rank Ġtest , Ġthe Ġperformance Ġof ĠJoint Ġis Ġstatistically Ġsignificant Ġ( Ġ$ p < 0 . 05 $ Ġ). ĠThis Ġshows Ġthat Ġthe Ġtextual Ġand Ġvisual Ġfeatures Ġcomplement Ġeach Ġother , Ġachieving Ġstate - of - the - art Ġresults Ġin Ġcombination . </s> For Ġar X iv , Ġbaseline Ġmethods ĠMajority , ĠBench mark , Ġand ĠIn ception Ġ$ _{ \ text { fixed }} $ Ġoutper form Ġbi L ST M Ġover Ġcs . ai , Ġin Ġlarge Ġpart Ġbecause Ġof Ġthe Ġclass Ġimbalance Ġin Ġthis Ġdataset Ġ( 90 % Ġof Ġpapers Ġare Ġrejected ). ĠSurprisingly , ĠIn ception Ġ$ _{ \ text { fixed }} $ Ġis Ġbetter Ġthan ĠMajority Ġand ĠBench mark Ġover Ġthe Ġar X iv Ġcs . l g Ġsubset , Ġwhich Ġver ifies Ġthe Ġusefulness Ġof Ġvisual Ġfeatures , Ġeven Ġwhen Ġonly Ġthe Ġlast Ġlayer Ġis Ġfine - tun ed . ĠTable Ġ3 Ġalso Ġshows Ġthat ĠIn ception Ġand Ġbi L ST M Ġachieve Ġsimilar Ġperformance Ġon Ġar X iv , Ġshowing Ġthat Ġtextual Ġand Ġvisual Ġrepresentations Ġare Ġequally Ġdiscrim in ative : ĠIn ception Ġand Ġbi L ST M Ġare Ġindistinguishable Ġover Ġcs . cl ; Ġbi L ST M Ġachieves Ġ1 . 8 % Ġhigher Ġaccuracy Ġover Ġcs . l g , Ġwhile ĠIn ception Ġachieves Ġ1 . 3 % Ġhigher Ġaccuracy Ġover Ġcs . ai . ĠOnce Ġagain , Ġthe ĠJoint Ġmodel Ġachieves Ġthe Ġhighest Ġaccuracy Ġon Ġcs . ai Ġand Ġcs . cl Ġby Ġcombining Ġtextual Ġand Ġvisual Ġrepresentations Ġ( at Ġa Ġlevel Ġof Ġstatistical Ġsignificance Ġfor Ġcs . ai ). ĠThis , Ġagain , Ġconfirms Ġthat Ġtextual Ġand Ġvisual Ġfeatures Ġcomplement Ġeach Ġother , Ġand Ġtogether Ġthey Ġachieve Ġstate - of - the - art Ġresults . ĠOn Ġar X iv Ġcs . l g , ĠJoint Ġachieves Ġa Ġ0 . 6 % Ġhigher Ġaccuracy Ġthan ĠIn ception Ġby Ġcombining Ġvisual Ġfeatures Ġand Ġtextual Ġfeatures , Ġbut Ġbi L ST M Ġachieves Ġthe Ġhighest Ġaccuracy . ĠOne Ġcharacteristic Ġof Ġcs . l g Ġdocuments Ġis Ġthat Ġthey Ġtend Ġto Ġcontain Ġmore Ġequations Ġthan Ġthe Ġother Ġtwo Ġar X iv Ġdatasets , Ġand Ġpreliminary Ġanalysis Ġsuggests Ġthat Ġthe Ġbi L ST M Ġis Ġpicking Ġup Ġon Ġa Ġcorrelation Ġbetween Ġthe Ġvolume / style Ġof Ġmathematical Ġpresentation Ġand Ġthe Ġquality Ġof Ġthe Ġdocument . </s> Analysis </s> In Ġthis Ġsection , Ġwe Ġfirst Ġanalyze Ġthe Ġperformance Ġof ĠIn ception Ġand ĠJoint . ĠWe Ġalso Ġanalyze Ġthe Ġperformance Ġof Ġdifferent Ġmodels Ġon Ġdifferent Ġquality Ġclasses . ĠThe Ġhigh - level Ġrepresentations Ġlearned Ġby Ġdifferent Ġmodels Ġare Ġalso Ġvisual ized Ġand Ġdiscussed . ĠAs Ġthe ĠWikipedia Ġtest Ġset Ġis Ġlarger Ġand Ġmore Ġbalanced Ġthan Ġthat Ġof Ġar X iv , Ġour Ġanalysis Ġwill Ġfocus Ġon ĠWikipedia . </s> In ception </s> To Ġbetter Ġunderstand Ġthe Ġperformance Ġof ĠIn ception , Ġwe Ġgenerated Ġthe Ġgradient - based Ġclass Ġactivation Ġmap ĠB IB REF 41 Ġ, Ġby Ġmaximizing Ġthe Ġoutputs Ġof Ġeach Ġclass Ġin Ġthe Ġpen ultimate Ġlayer , Ġas Ġshown Ġin ĠFigure Ġ3 Ġ. ĠFrom ĠFigure Ġ3 Ġand ĠFigure Ġ3 Ġ, Ġwe Ġcan Ġsee Ġthat ĠIn ception Ġidentifies Ġthe Ġtwo Ġmost Ġimportant Ġregions Ġ( one Ġat Ġthe Ġtop Ġcorresponding Ġto Ġthe Ġtable Ġof Ġcontents , Ġand Ġthe Ġother Ġat Ġthe Ġbottom , Ġcapturing Ġboth Ġdocument Ġlength Ġand Ġreferences ) Ġthat Ġcontribute Ġto Ġthe ĠFA Ġclass Ġprediction , Ġand Ġa Ġregion Ġin Ġthe Ġupper Ġhalf Ġof Ġthe Ġimage Ġthat Ġcontributes Ġto Ġthe ĠGA Ġclass Ġprediction Ġ( capt uring Ġthe Ġlength Ġof Ġthe Ġarticle Ġbody ). ĠFrom ĠFigure Ġ3 Ġand ĠFigure Ġ3 Ġ, Ġwe Ġcan Ġsee Ġthat Ġthe Ġmost Ġimportant Ġregions Ġin Ġterms Ġof ĠB Ġand ĠC Ġclass Ġprediction Ġcapture Ġimages Ġ( down Ġthe Ġleft Ġand Ġright Ġof Ġthe Ġpage , Ġin Ġthe Ġcase Ġof ĠB Ġand ĠC ), Ġand Ġdocument Ġlength / ref erences . ĠFrom ĠFigure Ġ3 Ġand ĠFigure Ġ3 Ġ, Ġwe Ġcan Ġsee Ġthat ĠIn ception Ġfinds Ġthat Ġimages Ġin Ġthe Ġtop Ġright Ġcorner Ġare Ġthe Ġstrongest Ġpredictor Ġof ĠStart Ġclass Ġprediction , Ġand Ġ( the Ġlack Ġof ) Ġimages / the Ġlink Ġbar Ġdown Ġthe Ġleft Ġside Ġof Ġthe Ġdocument Ġare Ġthe Ġmost Ġimportant Ġfor ĠStub Ġclass Ġprediction . </s> J oint </s> Table Ġ4 Ġshows Ġthe Ġconfusion Ġmatrix Ġof ĠJoint Ġon ĠWikipedia . ĠWe Ġcan Ġsee Ġthat Ġmore Ġthan Ġ50 % Ġof Ġdocuments Ġfor Ġeach Ġquality Ġclass Ġare Ġcorrectly Ġclassified , Ġexcept Ġfor Ġthe ĠC Ġclass Ġwhere Ġmore Ġdocuments Ġare Ġmis classified Ġinto ĠB . ĠAnalysis Ġshows Ġthat Ġwhen Ġmis classified , Ġdocuments Ġare Ġusually Ġmis classified Ġinto Ġadjacent Ġquality Ġclasses , Ġwhich Ġcan Ġbe Ġexplained Ġby Ġthe ĠWikipedia Ġgrading Ġscheme , Ġwhere Ġthe Ġcriteria Ġfor Ġadjacent Ġquality Ġclasses Ġare Ġmore Ġsimilar . </s> We Ġalso Ġprovide Ġa Ġbreakdown Ġof Ġprecision Ġ( âĢ ľ Ġ$\ math cal Ġ{ P } $ ĠâĢ Ŀ ), Ġrecall Ġ( âĢ ľ Ġ$\ math cal Ġ{ R } $ ĠâĢ Ŀ ), Ġand ĠF 1 Ġscore Ġ( âĢ ľ Ġ$\ math cal Ġ{ F } _{ \ beta Ġ= 1 } $ ĠâĢ Ŀ ) Ġfor Ġbi L ST M , ĠIn ception , Ġand ĠJoint Ġacross Ġthe Ġquality Ġclasses Ġin ĠTable Ġ5 Ġ. ĠWe Ġcan Ġsee Ġthat ĠJoint Ġachieves Ġthe Ġhighest Ġaccuracy Ġin Ġ11 Ġout Ġof Ġ18 Ġcases . ĠIt Ġis Ġalso Ġworth Ġnoting Ġthat Ġall Ġmodels Ġachieve Ġhigher Ġscores Ġfor ĠFA , ĠGA , Ġand ĠStub Ġarticles Ġthan ĠB , ĠC Ġand ĠStart Ġarticles . ĠThis Ġcan Ġbe Ġexplained Ġin Ġpart Ġby Ġthe Ġfact Ġthat ĠFA Ġand ĠGA Ġarticles Ġmust Ġpass Ġan Ġofficial Ġreview Ġbased Ġon Ġstructured Ġcriteria , Ġand Ġin Ġpart Ġby Ġthe Ġfact Ġthat ĠStub Ġarticles Ġare Ġusually Ġvery Ġshort , Ġwhich Ġis Ġdiscrim in ative Ġfor ĠIn ception , Ġand ĠJoint . ĠAll Ġmodels Ġperform Ġworst Ġon Ġthe ĠB Ġand ĠC Ġquality Ġclasses . ĠIt Ġis Ġdifficult Ġto Ġdifferentiate ĠB Ġarticles Ġfrom ĠC Ġarticles Ġeven Ġfor ĠWikipedia Ġcontributors . ĠAs Ġevidence Ġof Ġthis , Ġwhen Ġwe Ġcrawled Ġa Ġnew Ġdataset Ġincluding Ġtalk Ġpages Ġwith Ġquality Ġclass Ġvotes Ġfrom ĠWikipedia Ġcontributors , Ġwe Ġfound Ġthat Ġamong Ġarticles Ġwith Ġthree Ġor Ġmore Ġquality Ġlabels , Ġover Ġ20 % Ġpercent Ġof ĠB Ġand ĠC Ġarticles Ġhave Ġinconsistent Ġvotes Ġfrom ĠWikipedia Ġcontributors , Ġwhereas Ġfor ĠFA Ġand ĠGA Ġarticles Ġthe Ġnumber Ġis Ġonly Ġ0 . 7 %. </s> We Ġfurther Ġvisualize Ġthe Ġlearned Ġdocument Ġrepresentations Ġof Ġbi L ST M , ĠIn ception , Ġand ĠJoint Ġin Ġthe Ġform Ġof Ġa Ġt - S NE Ġplot ĠB IB REF 42 Ġin ĠFigure Ġ4 Ġ. ĠThe Ġdegree Ġof Ġseparation Ġbetween ĠStart Ġand ĠStub Ġachieved Ġby ĠIn ception Ġis Ġmuch Ġgreater Ġthan Ġfor Ġbi L ST M , Ġwith Ġthe Ġseparation Ġbetween ĠStart Ġand ĠStub Ġachieved Ġby ĠJoint Ġbeing Ġthe Ġcle arest Ġamong Ġthe Ġthree Ġmodels . ĠIn ception Ġand ĠJoint Ġare Ġbetter Ġthan Ġbi L ST M Ġat Ġseparating ĠStart Ġand ĠC . ĠJoint Ġachieves Ġslightly Ġbetter Ġperformance Ġthan ĠIn ception Ġin Ġseparating ĠGA Ġand ĠFA . ĠWe Ġcan Ġalso Ġsee Ġthat Ġit Ġis Ġdifficult Ġfor Ġall Ġmodels Ġto Ġseparate ĠB Ġand ĠC , Ġwhich Ġis Ġconsistent Ġwith Ġthe Ġfindings Ġof ĠTables Ġ4 Ġand Ġ5 Ġ. </s> Con clusions </s> We Ġproposed Ġto Ġuse Ġvisual Ġrender ings Ġof Ġdocuments Ġto Ġcapture Ġimplicit Ġdocument Ġquality Ġindicators , Ġsuch Ġas Ġfont Ġchoices , Ġimages , Ġand Ġvisual Ġlayout , Ġwhich Ġare Ġnot Ġcaptured Ġin Ġtextual Ġcontent . ĠWe Ġapplied Ġneural Ġnetwork Ġmodels Ġto Ġcapture Ġvisual Ġfeatures Ġgiven Ġvisual Ġrender ings Ġof Ġdocuments . ĠExperimental Ġresults Ġshow Ġthat Ġwe Ġachieve Ġa Ġ2 . 9 % Ġhigher Ġaccuracy Ġthan Ġstate - of - the - art Ġapproaches Ġbased Ġon Ġtextual Ġfeatures Ġover ĠWikipedia , Ġand Ġperformance Ġcompetitive Ġwith Ġor Ġsurpass ing Ġstate - of - the - art Ġapproaches Ġover Ġar X iv . ĠWe Ġfurther Ġproposed Ġa Ġjoint Ġmodel , Ġcombining Ġtextual Ġand Ġvisual Ġrepresentations , Ġto Ġpredict Ġthe Ġquality Ġof Ġa Ġdocument . ĠExperimental Ġresults Ġshow Ġthat Ġour Ġjoint Ġmodel Ġoutper forms Ġthe Ġvisual - only Ġmodel Ġin Ġall Ġcases , Ġand Ġthe Ġtext - only Ġmodel Ġon ĠWikipedia Ġand Ġtwo Ġsubs ets Ġof Ġar X iv . ĠThese Ġresults Ġunder line Ġthe Ġfeasibility Ġof Ġassessing Ġdocument Ġquality Ġvia Ġvisual Ġfeatures , Ġand Ġthe Ġcomplement arity Ġof Ġvisual Ġand Ġtextual Ġdocument Ġrepresentations Ġfor Ġquality Ġassessment .
Introduction The task of document quality assessment is to automatically assess a document according to some predefined inventory of quality labels. This can take many forms, including essay scoring (quality = language quality, coherence, and relevance to a topic), job application filtering (quality = suitability for role + visual/presentational quality of the application), or answer selection in community question answering (quality = actionability + relevance of the answer to the question). In the case of this paper, we focus on document quality assessment in two contexts: Wikipedia document quality classification, and whether a paper submitted to a conference was accepted or not. Automatic quality assessment has obvious benefits in terms of time savings and tractability in contexts where the volume of documents is large. In the case of dynamic documents (possibly with multiple authors), such as in the case of Wikipedia, it is particularly pertinent, as any edit potentially has implications for the quality label of that document (and around 10 English Wikipedia documents are edited per second). Furthermore, when the quality assessment task is decentralized (as in the case of Wikipedia and academic paper assessment), quality criteria are often applied inconsistently by different people, where an automatic document quality assessment system could potentially reduce inconsistencies and enable immediate author feedback. Current studies on document quality assessment mainly focus on textual features. For example, BIBREF0 examine features such as the article length and the number of headings to predict the quality class of a Wikipedia article. In contrast to these studies, in this paper, we propose to combine text features with visual features, based on a visual rendering of the document. Figure 1 illustrates our intuition, relative to Wikipedia articles. Without being able to read the text, we can tell that the article in Figure 1 has higher quality than Figure 1 , as it has a detailed infobox, extensive references, and a variety of images. Based on this intuition, we aim to answer the following question: can we achieve better accuracy on document quality assessment by complementing textual features with visual features? Our visual model is based on fine-tuning an Inception V3 model BIBREF1 over visual renderings of documents, while our textual model is based on a hierarchical biLSTM. We further combine the two into a joint model. We perform experiments on two datasets: a Wikipedia dataset novel to this paper, and an arXiv dataset provided by BIBREF2 split into three sub-parts based on subject category. Experimental results on the visual renderings of documents show that implicit quality indicators, such as images and visual layout, can be captured by an image classifier, at a level comparable to a text classifier. When we combine the two models, we achieve state-of-the-art results over 3/4 of our datasets. This paper makes the following contributions: All code and data associated with this research will be released on publication. Related Work A variety of approaches have been proposed for document quality assessment across different domains: Wikipedia article quality assessment, academic paper rating, content quality assessment in community question answering (cQA), and essay scoring. Among these approaches, some use hand-crafted features while others use neural networks to learn features from documents. For each domain, we first briefly describe feature-based approaches and then review neural network-based approaches. Wikipedia article quality assessment: Quality assessment of Wikipedia articles is a task that assigns a quality class label to a given Wikipedia article, mirroring the quality assessment process that the Wikipedia community carries out manually. Many approaches have been proposed that use features from the article itself, meta-data features (e.g., the editors, and Wikipedia article revision history), or a combination of the two. Article-internal features capture information such as whether an article is properly organized, with supporting evidence, and with appropriate terminology. For example, BIBREF3 use writing styles represented by binarized character trigram features to identify featured articles. BIBREF4 and BIBREF0 explore the number of headings, images, and references in the article. BIBREF5 use nine readability scores, such as the percentage of difficult words in the document, to measure the quality of the article. Meta-data features, which are indirect indicators of article quality, are usually extracted from revision history, and the interaction between editors and articles. For example, one heuristic that has been proposed is that higher-quality articles have more edits BIBREF6 , BIBREF7 . BIBREF8 use the percentage of registered editors and the total number of editors of an article. Article–editor dependencies have also been explored. For example, BIBREF9 use the authority of editors to measure the quality of Wikipedia articles, where the authority of editors is determined by the articles they edit. Deep learning approaches to predicting Wikipedia article quality have also been proposed. For example, BIBREF10 use a version of doc2vec BIBREF11 to represent articles, and feed the document embeddings into a four hidden layer neural network. BIBREF12 first obtain sentence representations by averaging words within a sentence, and then apply a biLSTM BIBREF13 to learn a document-level representation, which is combined with hand-crafted features as side information. BIBREF14 exploit two stacked biLSTMs to learn document representations. Academic paper rating: Academic paper rating is a relatively new task in NLP/AI, with the basic formulation being to automatically predict whether to accept or reject a paper. BIBREF2 explore hand-crafted features, such as the length of the title, whether specific words (such as outperform, state-of-the-art, and novel) appear in the abstract, and an embedded representation of the abstract as input to different downstream learners, such as logistic regression, decision tree, and random forest. BIBREF15 exploit a modularized hierarchical convolutional neural network (CNN), where each paper section is treated as a module. For each paper section, they train an attention-based CNN, and an attentive pooling layer is applied to the concatenated representation of each section, which is then fed into a softmax layer. Content quality assessment in cQA: Automatic quality assessment in cQA is the task of determining whether an answer is of high quality, selected as the best answer, or ranked higher than other answers. To measure answer content quality in cQA, researchers have exploited various features from different sources, such as the answer content itself, the answerer's profile, interactions among users, and usage of the content. The most common feature used is the answer length BIBREF16 , BIBREF17 , with other features including: syntactic and semantic features, such as readability scores. BIBREF18 ; similarity between the question and the answer at lexical, syntactic, and semantic levels BIBREF18 , BIBREF19 , BIBREF20 ; or user data (e.g., a user's status points or the number of answers written by the user). There have also been approaches using neural networks. For example, BIBREF21 combine CNN-learned representations with hand-crafted features to predict answer quality. BIBREF22 use a 2-dimensional CNN to learn the semantic relevance of an answer to the question, and apply an LSTM to the answer sequence to model thread context. BIBREF23 and BIBREF24 model the problem similarly to machine translation quality estimation, treating answers as competing translation hypotheses and the question as the reference translation, and apply neural machine translation to the problem. Essay scoring: Automated essay scoring is the task of assigning a score to an essay, usually in the context of assessing the language ability of a language learner. The quality of an essay is affected by the following four primary dimensions: topic relevance, organization and coherence, word usage and sentence complexity, and grammar and mechanics. To measure whether an essay is relevant to its “prompt” (the description of the essay topic), lexical and semantic overlap is commonly used BIBREF25 , BIBREF26 . BIBREF27 explore word features, such as the number of verb formation errors, average word frequency, and average word length, to measure word usage and lexical complexity. BIBREF28 use sentence structure features to measure sentence variety. The effects of grammatical and mechanic errors on the quality of an essay are measured via word and part-of-speech $n$ -gram features and “mechanics” features BIBREF29 (e.g., spelling, capitalization, and punctuation), respectively. BIBREF30 , BIBREF31 , and BIBREF32 use an LSTM to obtain an essay representation, which is used as the basis for classification. Similarly, BIBREF33 utilize a CNN to obtain sentence representation and an LSTM to obtain essay representation, with an attention layer at both the sentence and essay levels. The Proposed Joint Model We treat document quality assessment as a classification problem, i.e., given a document, we predict its quality class (e.g., whether an academic paper should be accepted or rejected). The proposed model is a joint model that integrates visual features learned through Inception V3 with textual features learned through a biLSTM. In this section, we present the details of the visual and textual embeddings, and finally describe how we combine the two. We return to discuss hyper-parameter settings and the experimental configuration in the Experiments section. Visual Embedding Learning A wide range of models have been proposed to tackle the image classification task, such as VGG BIBREF34 , ResNet BIBREF35 , Inception V3 BIBREF1 , and Xception BIBREF36 . However, to the best of our knowledge, there is no existing work that has proposed to use visual renderings of documents to assess document quality. In this paper, we use Inception V3 pretrained on ImageNet (“Inception” hereafter) to obtain visual embeddings of documents, noting that any image classifier could be applied to our task. The input to Inception is a visual rendering (screenshot) of a document, and the output is a visual embedding, which we will later integrate with our textual embedding. Based on the observation that it is difficult to decide what types of convolution to apply to each layer (such as 3 $\times $ 3 or 5 $\times $ 5), the basic Inception model applies multiple convolution filters in parallel and concatenates the resulting features, which are fed into the next layer. This has the benefit of capturing both local features through smaller convolutions and abstracted features through larger convolutions. Inception is a hybrid of multiple Inception models of different architectures. To reduce computational cost, Inception also modifies the basic model by applying a 1 $\times $ 1 convolution to the input and factorizing larger convolutions into smaller ones. Textual Embedding Learning We adopt a bi-directional LSTM model to generate textual embeddings for document quality assessment, following the method of BIBREF12 (“biLSTM” hereafter). The input to biLSTM is a textual document, and the output is a textual embedding, which will later integrate with the visual embedding. For biLSTM, each word is represented as a word embedding BIBREF37 , and an average-pooling layer is applied to the word embeddings to obtain the sentence embedding, which is fed into a bi-directional LSTM to generate the document embedding from the sentence embeddings. Then a max-pooling layer is applied to select the most salient features from the component sentences. The Joint Model The proposed joint model (“Joint” hereafter) combines the visual and textual embeddings (output of Inception and biLSTM) via a simple feed-forward layer and softmax over the document label set, as shown in Figure 2 . We optimize our model based on cross-entropy loss. Experiments In this section, we first describe the two datasets used in our experiments: (1) Wikipedia, and (2) arXiv. Then, we report the experimental details and results. Datasets The Wikipedia dataset consists of articles from English Wikipedia, with quality class labels assigned by the Wikipedia community. Wikipedia articles are labelled with one of six quality classes, in descending order of quality: Featured Article (“FA”), Good Article (“GA”), B-class Article (“B”), C-class Article (“C”), Start Article (“Start”), and Stub Article (“Stub”). A description of the criteria associated with the different classes can be found in the Wikipedia grading scheme page. The quality class of a Wikipedia article is assigned by Wikipedia reviewers or any registered user, who can discuss through the article's talk page to reach consensus. We constructed the dataset by first crawling all articles from each quality class repository, e.g., we get FA articles by crawling pages from the FA repository: https://en.wikipedia.org/wiki/Category:Featured_articles. This resulted in around 5K FA, 28K GA, 212K B, 533K C, 2.6M Start, and 3.2M Stub articles. We randomly sampled 5,000 articles from each quality class and removed all redirect pages, resulting in a dataset of 29,794 articles. As the wikitext contained in each document contains markup relating to the document category such as {Featured Article} or {geo-stub}, which reveals the label, we remove such information. We additionally randomly partitioned this dataset into training, development, and test splits based on a ratio of 8:1:1. Details of the dataset are summarized in Table 1 . We generate a visual representation of each document via a 1,000 $\times $ 2,000-pixel screenshot of the article via a PhantomJS script over the rendered version of the article, ensuring that the screenshot and wikitext versions of the article are the same version. Any direct indicators of document quality (such as the FA indicator, which is a bronze star icon in the top right corner of the webpage) are removed from the screenshot. The arXiv dataset BIBREF2 consists of three subsets of academic articles under the arXiv repository of Computer Science (cs), from the three subject areas of: Artificial Intelligence (cs.ai), Computation and Language (cs.cl), and Machine Learning (cs.lg). In line with the original dataset formulation BIBREF2 , a paper is considered to have been accepted (i.e. is positively labeled) if it matches a paper in the DBLP database or is otherwise accepted by any of the following conferences: ACL, EMNLP, NAACL, EACL, TACL, NIPS, ICML, ICLR, or AAAI. Failing this, it is considered to be rejected (noting that some of the papers may not have been submitted to one of these conferences). The median numbers of pages for papers in cs.ai, cs.cl, and cs.lg are 11, 10, and 12, respectively. To make sure each page in the PDF file has the same size in the screenshot, we crop the PDF file of a paper to the first 12; we pad the PDF file with blank pages if a PDF file has less than 12 pages, using the PyPDF2 Python package. We then use ImageMagick to convert the 12-page PDF file to a single 1,000 $\times $ 2,000 pixel screenshot. Table 2 details this dataset, where the “Accepted” column denotes the percentage of positive instances (accepted papers) in each subset. Experimental Setting As discussed above, our model has two main components — biLSTM and Inception— which generate textual and visual representations, respectively. For the biLSTM component, the documents are preprocessed as described in BIBREF12 , where an article is divided into sentences and tokenized using NLTK BIBREF38 . Words appearing more than 20 times are retained when building the vocabulary. All other words are replaced by the special UNK token. We use the pre-trained GloVe BIBREF39 50-dimensional word embeddings to represent words. For words not in GloVe, word embeddings are randomly initialized based on sampling from a uniform distribution $U(-1, 1)$ . All word embeddings are updated in the training process. We set the LSTM hidden layer size to 256. The concatenation of the forward and backward LSTMs thus gives us 512 dimensions for the document embedding. A dropout layer is applied at the sentence and document level, respectively, with a probability of 0.5. For Inception, we adopt data augmentation techniques in the training with a “nearest” filling mode, a zoom range of 0.1, a width shift range of 0.1, and a height shift range of 0.1. As the original screenshots have the size of 1,000 $\times 2$ ,000 pixels, they are resized to 500 $\times $ 500 to feed into Inception, where the input shape is (500, 500, 3). A dropout layer is applied with a probability of 0.5. Then, a GlobalAveragePooling2D layer is applied, which produces a 2,048 dimensional representation. For the Joint model, we get a representation of 2,560 dimensions by concatenating the 512 dimensional representation from the biLSTM with the 2,048 dimensional representation from Inception. The dropout layer is applied to the two components with a probability of 0.5. For biLSTM, we use a mini-batch size of 128 and a learning rate of 0.001. For both Inception and joint model, we use a mini-batch size of 16 and a learning rate of 0.0001. All hyper-parameters were set empirically over the development data, and the models were optimized using the Adam optimizer BIBREF40 . In the training phase, the weights in Inception are initialized by parameters pretrained on ImageNet, and the weights in biLSTM are randomly initialized (except for the word embeddings). We train each model for 50 epochs. However, to prevent overfitting, we adopt early stopping, where we stop training the model if the performance on the development set does not improve for 20 epochs. For evaluation, we use (micro-)accuracy, following previous studies BIBREF5 , BIBREF2 . Baseline Approaches We compare our models against the following five baselines: Majority: the model labels all test samples with the majority class of the training data. Benchmark: a benchmark method from the literature. In the case of Wikipedia, this is BIBREF5 , who use structural features and readability scores as features to build a random forest classifier; for arXiv, this is BIBREF2 , who use hand-crafted features, such as the number of references and TF-IDF weighted bag-of-words in abstract, to build a classifier based on the best of logistic regression, multi-layer perception, and AdaBoost. Doc2Vec: doc2vec BIBREF11 to learn document embeddings with a dimension of 500, and a 4-layer feed-forward classification model on top of this, with 2000, 1000, 500, and 200 dimensions, respectively. biLSTM: first derive a sentence representation by averaging across words in a sentence, then feed the sentence representation into a biLSTM and a maxpooling layer over output sequence to learn a document level representation with a dimension of 512, which is used to predict document quality. Inception $_{\text{fixed}}$ : the frozen Inception model, where only parameters in the last layer are fine-tuned during training. The hyper-parameters of Benchmark, Doc2Vec, and biLSTM are based on the corresponding papers except that: (1) we fine-tune the feed forward layer of Doc2Vec on the development set and train the model 300 epochs on Wikipedia and 50 epochs on arXiv; (2) we do not use hand-crafted features for biLSTM as we want the baselines to be comparable to our models, and the main focus of this paper is not to explore the effects of hand-crafted features (e.g., see BIBREF12 ). Experimental Results Table 3 shows the performance of the different models over our two datasets, in the form of the average accuracy on the test set (along with the standard deviation) over 10 runs, with different random initializations. On Wikipedia, we observe that the performance of biLSTM, Inception, and Joint is much better than that of all four baselines. Inception achieves 2.9% higher accuracy than biLSTM. The performance of Joint achieves an accuracy of 59.4%, which is 5.3% higher than using textual features alone (biLSTM) and 2.4% higher than using visual features alone (Inception). Based on a one-tailed Wilcoxon signed-rank test, the performance of Joint is statistically significant ( $p<0.05$ ). This shows that the textual and visual features complement each other, achieving state-of-the-art results in combination. For arXiv, baseline methods Majority, Benchmark, and Inception $_{\text{fixed}}$ outperform biLSTM over cs.ai, in large part because of the class imbalance in this dataset (90% of papers are rejected). Surprisingly, Inception $_{\text{fixed}}$ is better than Majority and Benchmark over the arXiv cs.lg subset, which verifies the usefulness of visual features, even when only the last layer is fine-tuned. Table 3 also shows that Inception and biLSTM achieve similar performance on arXiv, showing that textual and visual representations are equally discriminative: Inception and biLSTM are indistinguishable over cs.cl; biLSTM achieves 1.8% higher accuracy over cs.lg, while Inception achieves 1.3% higher accuracy over cs.ai. Once again, the Joint model achieves the highest accuracy on cs.ai and cs.cl by combining textual and visual representations (at a level of statistical significance for cs.ai). This, again, confirms that textual and visual features complement each other, and together they achieve state-of-the-art results. On arXiv cs.lg, Joint achieves a 0.6% higher accuracy than Inception by combining visual features and textual features, but biLSTM achieves the highest accuracy. One characteristic of cs.lg documents is that they tend to contain more equations than the other two arXiv datasets, and preliminary analysis suggests that the biLSTM is picking up on a correlation between the volume/style of mathematical presentation and the quality of the document. Analysis In this section, we first analyze the performance of Inception and Joint. We also analyze the performance of different models on different quality classes. The high-level representations learned by different models are also visualized and discussed. As the Wikipedia test set is larger and more balanced than that of arXiv, our analysis will focus on Wikipedia. Inception To better understand the performance of Inception, we generated the gradient-based class activation map BIBREF41 , by maximizing the outputs of each class in the penultimate layer, as shown in Figure 3 . From Figure 3 and Figure 3 , we can see that Inception identifies the two most important regions (one at the top corresponding to the table of contents, and the other at the bottom, capturing both document length and references) that contribute to the FA class prediction, and a region in the upper half of the image that contributes to the GA class prediction (capturing the length of the article body). From Figure 3 and Figure 3 , we can see that the most important regions in terms of B and C class prediction capture images (down the left and right of the page, in the case of B and C), and document length/references. From Figure 3 and Figure 3 , we can see that Inception finds that images in the top right corner are the strongest predictor of Start class prediction, and (the lack of) images/the link bar down the left side of the document are the most important for Stub class prediction. Joint Table 4 shows the confusion matrix of Joint on Wikipedia. We can see that more than 50% of documents for each quality class are correctly classified, except for the C class where more documents are misclassified into B. Analysis shows that when misclassified, documents are usually misclassified into adjacent quality classes, which can be explained by the Wikipedia grading scheme, where the criteria for adjacent quality classes are more similar. We also provide a breakdown of precision (“ $\mathcal {P}$ ”), recall (“ $\mathcal {R}$ ”), and F1 score (“ $\mathcal {F}_{\beta =1}$ ”) for biLSTM, Inception, and Joint across the quality classes in Table 5 . We can see that Joint achieves the highest accuracy in 11 out of 18 cases. It is also worth noting that all models achieve higher scores for FA, GA, and Stub articles than B, C and Start articles. This can be explained in part by the fact that FA and GA articles must pass an official review based on structured criteria, and in part by the fact that Stub articles are usually very short, which is discriminative for Inception, and Joint. All models perform worst on the B and C quality classes. It is difficult to differentiate B articles from C articles even for Wikipedia contributors. As evidence of this, when we crawled a new dataset including talk pages with quality class votes from Wikipedia contributors, we found that among articles with three or more quality labels, over 20% percent of B and C articles have inconsistent votes from Wikipedia contributors, whereas for FA and GA articles the number is only 0.7%. We further visualize the learned document representations of biLSTM, Inception, and Joint in the form of a t-SNE plot BIBREF42 in Figure 4 . The degree of separation between Start and Stub achieved by Inception is much greater than for biLSTM, with the separation between Start and Stub achieved by Joint being the clearest among the three models. Inception and Joint are better than biLSTM at separating Start and C. Joint achieves slightly better performance than Inception in separating GA and FA. We can also see that it is difficult for all models to separate B and C, which is consistent with the findings of Tables 4 and 5 . Conclusions We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content. We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document. Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv. These results underline the feasibility of assessing document quality via visual features, and the complementarity of visual and textual document representations for quality assessment.
Which languages do they use?
c9bc6f53b941863e801280343afa14248521ce43
Which Ġlanguages Ġdo Ġthey Ġuse ?
English
<s> Which Ġlanguages Ġdo Ġthey Ġuse ? </s> Introduction </s> The Ġtask Ġof Ġdocument Ġquality Ġassessment Ġis Ġto Ġautomatically Ġassess Ġa Ġdocument Ġaccording Ġto Ġsome Ġpred efined Ġinventory Ġof Ġquality Ġlabels . ĠThis Ġcan Ġtake Ġmany Ġforms , Ġincluding Ġessay Ġscoring Ġ( quality Ġ= Ġlanguage Ġquality , Ġco herence , Ġand Ġrelevance Ġto Ġa Ġtopic ), Ġjob Ġapplication Ġfiltering Ġ( quality Ġ= Ġsuit ability Ġfor Ġrole Ġ+ Ġvisual / present ational Ġquality Ġof Ġthe Ġapplication ), Ġor Ġanswer Ġselection Ġin Ġcommunity Ġquestion Ġanswering Ġ( quality Ġ= Ġaction ability Ġ+ Ġrelevance Ġof Ġthe Ġanswer Ġto Ġthe Ġquestion ). ĠIn Ġthe Ġcase Ġof Ġthis Ġpaper , Ġwe Ġfocus Ġon Ġdocument Ġquality Ġassessment Ġin Ġtwo Ġcontexts : ĠWikipedia Ġdocument Ġquality Ġclassification , Ġand Ġwhether Ġa Ġpaper Ġsubmitted Ġto Ġa Ġconference Ġwas Ġaccepted Ġor Ġnot . </s> Aut omatic Ġquality Ġassessment Ġhas Ġobvious Ġbenefits Ġin Ġterms Ġof Ġtime Ġsavings Ġand Ġtract ability Ġin Ġcontexts Ġwhere Ġthe Ġvolume Ġof Ġdocuments Ġis Ġlarge . ĠIn Ġthe Ġcase Ġof Ġdynamic Ġdocuments Ġ( possibly Ġwith Ġmultiple Ġauthors ), Ġsuch Ġas Ġin Ġthe Ġcase Ġof ĠWikipedia , Ġit Ġis Ġparticularly Ġpertinent , Ġas Ġany Ġedit Ġpotentially Ġhas Ġimplications Ġfor Ġthe Ġquality Ġlabel Ġof Ġthat Ġdocument Ġ( and Ġaround Ġ10 ĠEnglish ĠWikipedia Ġdocuments Ġare Ġedited Ġper Ġsecond ). ĠFurthermore , Ġwhen Ġthe Ġquality Ġassessment Ġtask Ġis Ġdecentralized Ġ( as Ġin Ġthe Ġcase Ġof ĠWikipedia Ġand Ġacademic Ġpaper Ġassessment ), Ġquality Ġcriteria Ġare Ġoften Ġapplied Ġinconsist ently Ġby Ġdifferent Ġpeople , Ġwhere Ġan Ġautomatic Ġdocument Ġquality Ġassessment Ġsystem Ġcould Ġpotentially Ġreduce Ġinconsistencies Ġand Ġenable Ġimmediate Ġauthor Ġfeedback . </s> Current Ġstudies Ġon Ġdocument Ġquality Ġassessment Ġmainly Ġfocus Ġon Ġtextual Ġfeatures . ĠFor Ġexample , ĠB IB REF 0 Ġexamine Ġfeatures Ġsuch Ġas Ġthe Ġarticle Ġlength Ġand Ġthe Ġnumber Ġof Ġhead ings Ġto Ġpredict Ġthe Ġquality Ġclass Ġof Ġa ĠWikipedia Ġarticle . ĠIn Ġcontrast Ġto Ġthese Ġstudies , Ġin Ġthis Ġpaper , Ġwe Ġpropose Ġto Ġcombine Ġtext Ġfeatures Ġwith Ġvisual Ġfeatures , Ġbased Ġon Ġa Ġvisual Ġrendering Ġof Ġthe Ġdocument . ĠFigure Ġ1 Ġillustrates Ġour Ġintuition , Ġrelative Ġto ĠWikipedia Ġarticles . ĠWithout Ġbeing Ġable Ġto Ġread Ġthe Ġtext , Ġwe Ġcan Ġtell Ġthat Ġthe Ġarticle Ġin ĠFigure Ġ1 Ġhas Ġhigher Ġquality Ġthan ĠFigure Ġ1 Ġ, Ġas Ġit Ġhas Ġa Ġdetailed Ġinf ob ox , Ġextensive Ġreferences , Ġand Ġa Ġvariety Ġof Ġimages . ĠBased Ġon Ġthis Ġintuition , Ġwe Ġaim Ġto Ġanswer Ġthe Ġfollowing Ġquestion : Ġcan Ġwe Ġachieve Ġbetter Ġaccuracy Ġon Ġdocument Ġquality Ġassessment Ġby Ġcomplement ing Ġtextual Ġfeatures Ġwith Ġvisual Ġfeatures ? </s> Our Ġvisual Ġmodel Ġis Ġbased Ġon Ġfine - tun ing Ġan ĠIn ception ĠV 3 Ġmodel ĠB IB REF 1 Ġover Ġvisual Ġrender ings Ġof Ġdocuments , Ġwhile Ġour Ġtextual Ġmodel Ġis Ġbased Ġon Ġa Ġhierarchical Ġbi L ST M . ĠWe Ġfurther Ġcombine Ġthe Ġtwo Ġinto Ġa Ġjoint Ġmodel . ĠWe Ġperform Ġexperiments Ġon Ġtwo Ġdatasets : Ġa ĠWikipedia Ġdataset Ġnovel Ġto Ġthis Ġpaper , Ġand Ġan Ġar X iv Ġdataset Ġprovided Ġby ĠB IB REF 2 Ġsplit Ġinto Ġthree Ġsub - parts Ġbased Ġon Ġsubject Ġcategory . ĠExperimental Ġresults Ġon Ġthe Ġvisual Ġrender ings Ġof Ġdocuments Ġshow Ġthat Ġimplicit Ġquality Ġindicators , Ġsuch Ġas Ġimages Ġand Ġvisual Ġlayout , Ġcan Ġbe Ġcaptured Ġby Ġan Ġimage Ġclass ifier , Ġat Ġa Ġlevel Ġcomparable Ġto Ġa Ġtext Ġclass ifier . ĠWhen Ġwe Ġcombine Ġthe Ġtwo Ġmodels , Ġwe Ġachieve Ġstate - of - the - art Ġresults Ġover Ġ3 / 4 Ġof Ġour Ġdatasets . </s> This Ġpaper Ġmakes Ġthe Ġfollowing Ġcontributions : </s> All Ġcode Ġand Ġdata Ġassociated Ġwith Ġthis Ġresearch Ġwill Ġbe Ġreleased Ġon Ġpublication . </s> Related ĠWork </s> A Ġvariety Ġof Ġapproaches Ġhave Ġbeen Ġproposed Ġfor Ġdocument Ġquality Ġassessment Ġacross Ġdifferent Ġdomains : ĠWikipedia Ġarticle Ġquality Ġassessment , Ġacademic Ġpaper Ġrating , Ġcontent Ġquality Ġassessment Ġin Ġcommunity Ġquestion Ġanswering Ġ( c Q A ), Ġand Ġessay Ġscoring . ĠAmong Ġthese Ġapproaches , Ġsome Ġuse Ġhand - crafted Ġfeatures Ġwhile Ġothers Ġuse Ġneural Ġnetworks Ġto Ġlearn Ġfeatures Ġfrom Ġdocuments . ĠFor Ġeach Ġdomain , Ġwe Ġfirst Ġbriefly Ġdescribe Ġfeature - based Ġapproaches Ġand Ġthen Ġreview Ġneural Ġnetwork - based Ġapproaches . ĠWikipedia Ġarticle Ġquality Ġassessment : ĠQuality Ġassessment Ġof ĠWikipedia Ġarticles Ġis Ġa Ġtask Ġthat Ġassigns Ġa Ġquality Ġclass Ġlabel Ġto Ġa Ġgiven ĠWikipedia Ġarticle , Ġmirror ing Ġthe Ġquality Ġassessment Ġprocess Ġthat Ġthe ĠWikipedia Ġcommunity Ġcarries Ġout Ġmanually . ĠMany Ġapproaches Ġhave Ġbeen Ġproposed Ġthat Ġuse Ġfeatures Ġfrom Ġthe Ġarticle Ġitself , Ġmeta - data Ġfeatures Ġ( e . g ., Ġthe Ġeditors , Ġand ĠWikipedia Ġarticle Ġrevision Ġhistory ), Ġor Ġa Ġcombination Ġof Ġthe Ġtwo . ĠArticle - internal Ġfeatures Ġcapture Ġinformation Ġsuch Ġas Ġwhether Ġan Ġarticle Ġis Ġproperly Ġorganized , Ġwith Ġsupporting Ġevidence , Ġand Ġwith Ġappropriate Ġterminology . ĠFor Ġexample , ĠB IB REF 3 Ġuse Ġwriting Ġstyles Ġrepresented Ġby Ġbin ar ized Ġcharacter Ġtrig ram Ġfeatures Ġto Ġidentify Ġfeatured Ġarticles . ĠB IB REF 4 Ġand ĠB IB REF 0 Ġexplore Ġthe Ġnumber Ġof Ġhead ings , Ġimages , Ġand Ġreferences Ġin Ġthe Ġarticle . ĠB IB REF 5 Ġuse Ġnine Ġread ability Ġscores , Ġsuch Ġas Ġthe Ġpercentage Ġof Ġdifficult Ġwords Ġin Ġthe Ġdocument , Ġto Ġmeasure Ġthe Ġquality Ġof Ġthe Ġarticle . ĠMeta - data Ġfeatures , Ġwhich Ġare Ġindirect Ġindicators Ġof Ġarticle Ġquality , Ġare Ġusually Ġextracted Ġfrom Ġrevision Ġhistory , Ġand Ġthe Ġinteraction Ġbetween Ġeditors Ġand Ġarticles . ĠFor Ġexample , Ġone Ġhe uristic Ġthat Ġhas Ġbeen Ġproposed Ġis Ġthat Ġhigher - quality Ġarticles Ġhave Ġmore Ġedits ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ. ĠB IB REF 8 Ġuse Ġthe Ġpercentage Ġof Ġregistered Ġeditors Ġand Ġthe Ġtotal Ġnumber Ġof Ġeditors Ġof Ġan Ġarticle . ĠArticle âĢĵ editor Ġdependencies Ġhave Ġalso Ġbeen Ġexplored . ĠFor Ġexample , ĠB IB REF 9 Ġuse Ġthe Ġauthority Ġof Ġeditors Ġto Ġmeasure Ġthe Ġquality Ġof ĠWikipedia Ġarticles , Ġwhere Ġthe Ġauthority Ġof Ġeditors Ġis Ġdetermined Ġby Ġthe Ġarticles Ġthey Ġedit . ĠDeep Ġlearning Ġapproaches Ġto Ġpredicting ĠWikipedia Ġarticle Ġquality Ġhave Ġalso Ġbeen Ġproposed . ĠFor Ġexample , ĠB IB REF 10 Ġuse Ġa Ġversion Ġof Ġdoc 2 vec ĠB IB REF 11 Ġto Ġrepresent Ġarticles , Ġand Ġfeed Ġthe Ġdocument Ġembed d ings Ġinto Ġa Ġfour Ġhidden Ġlayer Ġneural Ġnetwork . ĠB IB REF 12 Ġfirst Ġobtain Ġsentence Ġrepresentations Ġby Ġaveraging Ġwords Ġwithin Ġa Ġsentence , Ġand Ġthen Ġapply Ġa Ġbi L ST M ĠB IB REF 13 Ġto Ġlearn Ġa Ġdocument - level Ġrepresentation , Ġwhich Ġis Ġcombined Ġwith Ġhand - crafted Ġfeatures Ġas Ġside Ġinformation . ĠB IB REF 14 Ġexploit Ġtwo Ġstacked Ġbi L ST Ms Ġto Ġlearn Ġdocument Ġrepresentations . </s> Ac ademic Ġpaper Ġrating : ĠAcademic Ġpaper Ġrating Ġis Ġa Ġrelatively Ġnew Ġtask Ġin ĠN LP / AI , Ġwith Ġthe Ġbasic Ġformulation Ġbeing Ġto Ġautomatically Ġpredict Ġwhether Ġto Ġaccept Ġor Ġreject Ġa Ġpaper . ĠB IB REF 2 Ġexplore Ġhand - crafted Ġfeatures , Ġsuch Ġas Ġthe Ġlength Ġof Ġthe Ġtitle , Ġwhether Ġspecific Ġwords Ġ( such Ġas Ġoutper form , Ġstate - of - the - art , Ġand Ġnovel ) Ġappear Ġin Ġthe Ġabstract , Ġand Ġan Ġembedded Ġrepresentation Ġof Ġthe Ġabstract Ġas Ġinput Ġto Ġdifferent Ġdownstream Ġlearners , Ġsuch Ġas Ġlog istic Ġregression , Ġdecision Ġtree , Ġand Ġrandom Ġforest . ĠB IB REF 15 Ġexploit Ġa Ġmodular ized Ġhierarchical Ġconv olution al Ġneural Ġnetwork Ġ( CNN ), Ġwhere Ġeach Ġpaper Ġsection Ġis Ġtreated Ġas Ġa Ġmodule . ĠFor Ġeach Ġpaper Ġsection , Ġthey Ġtrain Ġan Ġattention - based ĠCNN , Ġand Ġan Ġattentive Ġpool ing Ġlayer Ġis Ġapplied Ġto Ġthe Ġconc aten ated Ġrepresentation Ġof Ġeach Ġsection , Ġwhich Ġis Ġthen Ġfed Ġinto Ġa Ġsoft max Ġlayer . </s> Content Ġquality Ġassessment Ġin Ġc Q A : ĠAutomatic Ġquality Ġassessment Ġin Ġc Q A Ġis Ġthe Ġtask Ġof Ġdetermining Ġwhether Ġan Ġanswer Ġis Ġof Ġhigh Ġquality , Ġselected Ġas Ġthe Ġbest Ġanswer , Ġor Ġranked Ġhigher Ġthan Ġother Ġanswers . ĠTo Ġmeasure Ġanswer Ġcontent Ġquality Ġin Ġc Q A , Ġresearchers Ġhave Ġexploited Ġvarious Ġfeatures Ġfrom Ġdifferent Ġsources , Ġsuch Ġas Ġthe Ġanswer Ġcontent Ġitself , Ġthe Ġansw erer 's Ġprofile , Ġinteractions Ġamong Ġusers , Ġand Ġusage Ġof Ġthe Ġcontent . ĠThe Ġmost Ġcommon Ġfeature Ġused Ġis Ġthe Ġanswer Ġlength ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, Ġwith Ġother Ġfeatures Ġincluding : Ġsynt actic Ġand Ġsemantic Ġfeatures , Ġsuch Ġas Ġread ability Ġscores . ĠB IB REF 18 Ġ; Ġsimilarity Ġbetween Ġthe Ġquestion Ġand Ġthe Ġanswer Ġat Ġlex ical , Ġsynt actic , Ġand Ġsemantic Ġlevels ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ; Ġor Ġuser Ġdata Ġ( e . g ., Ġa Ġuser 's Ġstatus Ġpoints Ġor Ġthe Ġnumber Ġof Ġanswers Ġwritten Ġby Ġthe Ġuser ). ĠThere Ġhave Ġalso Ġbeen Ġapproaches Ġusing Ġneural Ġnetworks . ĠFor Ġexample , ĠB IB REF 21 Ġcombine ĠCNN - learn ed Ġrepresentations Ġwith Ġhand - crafted Ġfeatures Ġto Ġpredict Ġanswer Ġquality . ĠB IB REF 22 Ġuse Ġa Ġ2 - dimensional ĠCNN Ġto Ġlearn Ġthe Ġsemantic Ġrelevance Ġof Ġan Ġanswer Ġto Ġthe Ġquestion , Ġand Ġapply Ġan ĠL ST M Ġto Ġthe Ġanswer Ġsequence Ġto Ġmodel Ġthread Ġcontext . ĠB IB REF 23 Ġand ĠB IB REF 24 Ġmodel Ġthe Ġproblem Ġsimilarly Ġto Ġmachine Ġtranslation Ġquality Ġestimation , Ġtreating Ġanswers Ġas Ġcompeting Ġtranslation Ġhypotheses Ġand Ġthe Ġquestion Ġas Ġthe Ġreference Ġtranslation , Ġand Ġapply Ġneural Ġmachine Ġtranslation Ġto Ġthe Ġproblem . ĠEss ay Ġscoring : ĠAutom ated Ġessay Ġscoring Ġis Ġthe Ġtask Ġof Ġassigning Ġa Ġscore Ġto Ġan Ġessay , Ġusually Ġin Ġthe Ġcontext Ġof Ġassessing Ġthe Ġlanguage Ġability Ġof Ġa Ġlanguage Ġlear ner . ĠThe Ġquality Ġof Ġan Ġessay Ġis Ġaffected Ġby Ġthe Ġfollowing Ġfour Ġprimary Ġdimensions : Ġtopic Ġrelevance , Ġorganization Ġand Ġco herence , Ġword Ġusage Ġand Ġsentence Ġcomplexity , Ġand Ġgrammar Ġand Ġmechanics . ĠTo Ġmeasure Ġwhether Ġan Ġessay Ġis Ġrelevant Ġto Ġits ĠâĢ ľ prom pt âĢ Ŀ Ġ( the Ġdescription Ġof Ġthe Ġessay Ġtopic ), Ġlex ical Ġand Ġsemantic Ġoverlap Ġis Ġcommonly Ġused ĠB IB REF 25 Ġ, ĠB IB REF 26 Ġ. ĠB IB REF 27 Ġexplore Ġword Ġfeatures , Ġsuch Ġas Ġthe Ġnumber Ġof Ġverb Ġformation Ġerrors , Ġaverage Ġword Ġfrequency , Ġand Ġaverage Ġword Ġlength , Ġto Ġmeasure Ġword Ġusage Ġand Ġlex ical Ġcomplexity . ĠB IB REF 28 Ġuse Ġsentence Ġstructure Ġfeatures Ġto Ġmeasure Ġsentence Ġvariety . ĠThe Ġeffects Ġof Ġgram matical Ġand Ġmechanic Ġerrors Ġon Ġthe Ġquality Ġof Ġan Ġessay Ġare Ġmeasured Ġvia Ġword Ġand Ġpart - of - speech Ġ$ n $ Ġ- gram Ġfeatures Ġand ĠâĢ ľ me chan ics âĢ Ŀ Ġfeatures ĠB IB REF 29 Ġ( e . g ., Ġspelling , Ġcapital ization , Ġand Ġpunct uation ), Ġrespectively . ĠB IB REF 30 Ġ, ĠB IB REF 31 Ġ, Ġand ĠB IB REF 32 Ġuse Ġan ĠL ST M Ġto Ġobtain Ġan Ġessay Ġrepresentation , Ġwhich Ġis Ġused Ġas Ġthe Ġbasis Ġfor Ġclassification . ĠSimilarly , ĠB IB REF 33 Ġutilize Ġa ĠCNN Ġto Ġobtain Ġsentence Ġrepresentation Ġand Ġan ĠL ST M Ġto Ġobtain Ġessay Ġrepresentation , Ġwith Ġan Ġattention Ġlayer Ġat Ġboth Ġthe Ġsentence Ġand Ġessay Ġlevels . </s> The ĠProp osed ĠJoint ĠModel </s> We Ġtreat Ġdocument Ġquality Ġassessment Ġas Ġa Ġclassification Ġproblem , Ġi . e ., Ġgiven Ġa Ġdocument , Ġwe Ġpredict Ġits Ġquality Ġclass Ġ( e . g ., Ġwhether Ġan Ġacademic Ġpaper Ġshould Ġbe Ġaccepted Ġor Ġrejected ). ĠThe Ġproposed Ġmodel Ġis Ġa Ġjoint Ġmodel Ġthat Ġintegrates Ġvisual Ġfeatures Ġlearned Ġthrough ĠIn ception ĠV 3 Ġwith Ġtextual Ġfeatures Ġlearned Ġthrough Ġa Ġbi L ST M . ĠIn Ġthis Ġsection , Ġwe Ġpresent Ġthe Ġdetails Ġof Ġthe Ġvisual Ġand Ġtextual Ġembed d ings , Ġand Ġfinally Ġdescribe Ġhow Ġwe Ġcombine Ġthe Ġtwo . ĠWe Ġreturn Ġto Ġdiscuss Ġhyper - param eter Ġsettings Ġand Ġthe Ġexperimental Ġconfiguration Ġin Ġthe ĠExper iments Ġsection . </s> Visual ĠEmb edd ing ĠLearning </s> A Ġwide Ġrange Ġof Ġmodels Ġhave Ġbeen Ġproposed Ġto Ġtackle Ġthe Ġimage Ġclassification Ġtask , Ġsuch Ġas ĠV GG ĠB IB REF 34 Ġ, ĠRes Net ĠB IB REF 35 Ġ, ĠIn ception ĠV 3 ĠB IB REF 1 Ġ, Ġand ĠX ception ĠB IB REF 36 Ġ. ĠHowever , Ġto Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthere Ġis Ġno Ġexisting Ġwork Ġthat Ġhas Ġproposed Ġto Ġuse Ġvisual Ġrender ings Ġof Ġdocuments Ġto Ġassess Ġdocument Ġquality . ĠIn Ġthis Ġpaper , Ġwe Ġuse ĠIn ception ĠV 3 Ġpret rained Ġon ĠImage Net Ġ( âĢ ľ In ception âĢ Ŀ Ġhereafter ) Ġto Ġobtain Ġvisual Ġembed d ings Ġof Ġdocuments , Ġnoting Ġthat Ġany Ġimage Ġclass ifier Ġcould Ġbe Ġapplied Ġto Ġour Ġtask . ĠThe Ġinput Ġto ĠIn ception Ġis Ġa Ġvisual Ġrendering Ġ( sc reenshot ) Ġof Ġa Ġdocument , Ġand Ġthe Ġoutput Ġis Ġa Ġvisual Ġembed ding , Ġwhich Ġwe Ġwill Ġlater Ġintegrate Ġwith Ġour Ġtextual Ġembed ding . </s> Based Ġon Ġthe Ġobservation Ġthat Ġit Ġis Ġdifficult Ġto Ġdecide Ġwhat Ġtypes Ġof Ġconv olution Ġto Ġapply Ġto Ġeach Ġlayer Ġ( such Ġas Ġ3 Ġ$\ times Ġ$ Ġ3 Ġor Ġ5 Ġ$\ times Ġ$ Ġ5 ), Ġthe Ġbasic ĠIn ception Ġmodel Ġapplies Ġmultiple Ġconv olution Ġfilters Ġin Ġparallel Ġand Ġconc aten ates Ġthe Ġresulting Ġfeatures , Ġwhich Ġare Ġfed Ġinto Ġthe Ġnext Ġlayer . ĠThis Ġhas Ġthe Ġbenefit Ġof Ġcapturing Ġboth Ġlocal Ġfeatures Ġthrough Ġsmaller Ġconv olutions Ġand Ġabstract ed Ġfeatures Ġthrough Ġlarger Ġconv olutions . ĠIn ception Ġis Ġa Ġhybrid Ġof Ġmultiple ĠIn ception Ġmodels Ġof Ġdifferent Ġarchitectures . ĠTo Ġreduce Ġcomputational Ġcost , ĠIn ception Ġalso Ġmod ifies Ġthe Ġbasic Ġmodel Ġby Ġapplying Ġa Ġ1 Ġ$\ times Ġ$ Ġ1 Ġconv olution Ġto Ġthe Ġinput Ġand Ġfactor izing Ġlarger Ġconv olutions Ġinto Ġsmaller Ġones . </s> Text ual ĠEmb edd ing ĠLearning </s> We Ġadopt Ġa Ġbi - direction al ĠL ST M Ġmodel Ġto Ġgenerate Ġtextual Ġembed d ings Ġfor Ġdocument Ġquality Ġassessment , Ġfollowing Ġthe Ġmethod Ġof ĠB IB REF 12 Ġ( âĢ ľ bi L ST M âĢ Ŀ Ġhereafter ). ĠThe Ġinput Ġto Ġbi L ST M Ġis Ġa Ġtextual Ġdocument , Ġand Ġthe Ġoutput Ġis Ġa Ġtextual Ġembed ding , Ġwhich Ġwill Ġlater Ġintegrate Ġwith Ġthe Ġvisual Ġembed ding . </s> For Ġbi L ST M , Ġeach Ġword Ġis Ġrepresented Ġas Ġa Ġword Ġembed ding ĠB IB REF 37 Ġ, Ġand Ġan Ġaverage - pool ing Ġlayer Ġis Ġapplied Ġto Ġthe Ġword Ġembed d ings Ġto Ġobtain Ġthe Ġsentence Ġembed ding , Ġwhich Ġis Ġfed Ġinto Ġa Ġbi - direction al ĠL ST M Ġto Ġgenerate Ġthe Ġdocument Ġembed ding Ġfrom Ġthe Ġsentence Ġembed d ings . ĠThen Ġa Ġmax - pool ing Ġlayer Ġis Ġapplied Ġto Ġselect Ġthe Ġmost Ġsalient Ġfeatures Ġfrom Ġthe Ġcomponent Ġsentences . </s> The ĠJoint ĠModel </s> The Ġproposed Ġjoint Ġmodel Ġ( âĢ ľ J oint âĢ Ŀ Ġhereafter ) Ġcombines Ġthe Ġvisual Ġand Ġtextual Ġembed d ings Ġ( output Ġof ĠIn ception Ġand Ġbi L ST M ) Ġvia Ġa Ġsimple Ġfeed - forward Ġlayer Ġand Ġsoft max Ġover Ġthe Ġdocument Ġlabel Ġset , Ġas Ġshown Ġin ĠFigure Ġ2 Ġ. ĠWe Ġoptimize Ġour Ġmodel Ġbased Ġon Ġcross - ent ropy Ġloss . </s> Exper iments </s> In Ġthis Ġsection , Ġwe Ġfirst Ġdescribe Ġthe Ġtwo Ġdatasets Ġused Ġin Ġour Ġexperiments : Ġ( 1 ) ĠWikipedia , Ġand Ġ( 2 ) Ġar X iv . ĠThen , Ġwe Ġreport Ġthe Ġexperimental Ġdetails Ġand Ġresults . </s> Dat as ets </s> The ĠWikipedia Ġdataset Ġconsists Ġof Ġarticles Ġfrom ĠEnglish ĠWikipedia , Ġwith Ġquality Ġclass Ġlabels Ġassigned Ġby Ġthe ĠWikipedia Ġcommunity . ĠWikipedia Ġarticles Ġare Ġlabelled Ġwith Ġone Ġof Ġsix Ġquality Ġclasses , Ġin Ġdescending Ġorder Ġof Ġquality : ĠFeatured ĠArticle Ġ( âĢ ľ FA âĢ Ŀ ), ĠGood ĠArticle Ġ( âĢ ľ GA âĢ Ŀ ), ĠB - class ĠArticle Ġ( âĢ ľ B âĢ Ŀ ), ĠC - class ĠArticle Ġ( âĢ ľ C âĢ Ŀ ), ĠStart ĠArticle Ġ( âĢ ľ Start âĢ Ŀ ), Ġand ĠStub ĠArticle Ġ( âĢ ľ St ub âĢ Ŀ ). ĠA Ġdescription Ġof Ġthe Ġcriteria Ġassociated Ġwith Ġthe Ġdifferent Ġclasses Ġcan Ġbe Ġfound Ġin Ġthe ĠWikipedia Ġgrading Ġscheme Ġpage . ĠThe Ġquality Ġclass Ġof Ġa ĠWikipedia Ġarticle Ġis Ġassigned Ġby ĠWikipedia Ġreviewers Ġor Ġany Ġregistered Ġuser , Ġwho Ġcan Ġdiscuss Ġthrough Ġthe Ġarticle 's Ġtalk Ġpage Ġto Ġreach Ġconsensus . ĠWe Ġconstructed Ġthe Ġdataset Ġby Ġfirst Ġcrawling Ġall Ġarticles Ġfrom Ġeach Ġquality Ġclass Ġrepository , Ġe . g ., Ġwe Ġget ĠFA Ġarticles Ġby Ġcrawling Ġpages Ġfrom Ġthe ĠFA Ġrepository : Ġhttps :// en . wikipedia . org / wiki / Category : Featured _ articles . ĠThis Ġresulted Ġin Ġaround Ġ5 K ĠFA , Ġ28 K ĠGA , Ġ212 K ĠB , Ġ5 33 K ĠC , Ġ2 . 6 M ĠStart , Ġand Ġ3 . 2 M ĠStub Ġarticles . </s> We Ġrandomly Ġsampled Ġ5 , 000 Ġarticles Ġfrom Ġeach Ġquality Ġclass Ġand Ġremoved Ġall Ġredirect Ġpages , Ġresulting Ġin Ġa Ġdataset Ġof Ġ29 , 794 Ġarticles . ĠAs Ġthe Ġwik ite xt Ġcontained Ġin Ġeach Ġdocument Ġcontains Ġmarkup Ġrelating Ġto Ġthe Ġdocument Ġcategory Ġsuch Ġas Ġ{ Featured ĠArticle } Ġor Ġ{ ge o - st ub }, Ġwhich Ġreveals Ġthe Ġlabel , Ġwe Ġremove Ġsuch Ġinformation . ĠWe Ġadditionally Ġrandomly Ġpartition ed Ġthis Ġdataset Ġinto Ġtraining , Ġdevelopment , Ġand Ġtest Ġsplits Ġbased Ġon Ġa Ġratio Ġof Ġ8 : 1 : 1 . ĠDetails Ġof Ġthe Ġdataset Ġare Ġsummarized Ġin ĠTable Ġ1 Ġ. </s> We Ġgenerate Ġa Ġvisual Ġrepresentation Ġof Ġeach Ġdocument Ġvia Ġa Ġ1 , 000 Ġ$\ times Ġ$ Ġ2 , 000 - pixel Ġscreenshot Ġof Ġthe Ġarticle Ġvia Ġa ĠPhantom JS Ġscript Ġover Ġthe Ġrendered Ġversion Ġof Ġthe Ġarticle , Ġensuring Ġthat Ġthe Ġscreenshot Ġand Ġwik ite xt Ġversions Ġof Ġthe Ġarticle Ġare Ġthe Ġsame Ġversion . ĠAny Ġdirect Ġindicators Ġof Ġdocument Ġquality Ġ( such Ġas Ġthe ĠFA Ġindicator , Ġwhich Ġis Ġa Ġbronze Ġstar Ġicon Ġin Ġthe Ġtop Ġright Ġcorner Ġof Ġthe Ġwebpage ) Ġare Ġremoved Ġfrom Ġthe Ġscreenshot . </s> The Ġar X iv Ġdataset ĠB IB REF 2 Ġconsists Ġof Ġthree Ġsubs ets Ġof Ġacademic Ġarticles Ġunder Ġthe Ġar X iv Ġrepository Ġof ĠComputer ĠScience Ġ( cs ), Ġfrom Ġthe Ġthree Ġsubject Ġareas Ġof : ĠArtificial ĠIntelligence Ġ( cs . ai ), ĠComput ation Ġand ĠLanguage Ġ( cs . cl ), Ġand ĠMachine ĠLearning Ġ( cs . l g ). ĠIn Ġline Ġwith Ġthe Ġoriginal Ġdataset Ġformulation ĠB IB REF 2 Ġ, Ġa Ġpaper Ġis Ġconsidered Ġto Ġhave Ġbeen Ġaccepted Ġ( i . e . Ġis Ġpositively Ġlabeled ) Ġif Ġit Ġmatches Ġa Ġpaper Ġin Ġthe ĠD BL P Ġdatabase Ġor Ġis Ġotherwise Ġaccepted Ġby Ġany Ġof Ġthe Ġfollowing Ġconferences : ĠACL , ĠEM N LP , ĠNA AC L , ĠE AC L , ĠT AC L , ĠNI PS , ĠIC ML , ĠI CL R , Ġor ĠAAA I . ĠF ailing Ġthis , Ġit Ġis Ġconsidered Ġto Ġbe Ġrejected Ġ( not ing Ġthat Ġsome Ġof Ġthe Ġpapers Ġmay Ġnot Ġhave Ġbeen Ġsubmitted Ġto Ġone Ġof Ġthese Ġconferences ). ĠThe Ġmedian Ġnumbers Ġof Ġpages Ġfor Ġpapers Ġin Ġcs . ai , Ġcs . cl , Ġand Ġcs . l g Ġare Ġ11 , Ġ10 , Ġand Ġ12 , Ġrespectively . ĠTo Ġmake Ġsure Ġeach Ġpage Ġin Ġthe ĠPDF Ġfile Ġhas Ġthe Ġsame Ġsize Ġin Ġthe Ġscreenshot , Ġwe Ġcrop Ġthe ĠPDF Ġfile Ġof Ġa Ġpaper Ġto Ġthe Ġfirst Ġ12 ; Ġwe Ġpad Ġthe ĠPDF Ġfile Ġwith Ġblank Ġpages Ġif Ġa ĠPDF Ġfile Ġhas Ġless Ġthan Ġ12 Ġpages , Ġusing Ġthe ĠPy PDF 2 ĠPython Ġpackage . ĠWe Ġthen Ġuse ĠImage Mag ick Ġto Ġconvert Ġthe Ġ12 - page ĠPDF Ġfile Ġto Ġa Ġsingle Ġ1 , 000 Ġ$\ times Ġ$ Ġ2 , 000 Ġpixel Ġscreenshot . ĠTable Ġ2 Ġdetails Ġthis Ġdataset , Ġwhere Ġthe ĠâĢ ľ Accept ed âĢ Ŀ Ġcolumn Ġdenotes Ġthe Ġpercentage Ġof Ġpositive Ġinstances Ġ( accept ed Ġpapers ) Ġin Ġeach Ġsubset . </s> Exper imental ĠSetting </s> As Ġdiscussed Ġabove , Ġour Ġmodel Ġhas Ġtwo Ġmain Ġcomponents ĠâĢĶ Ġbi L ST M Ġand ĠIn ception âĢĶ Ġwhich Ġgenerate Ġtextual Ġand Ġvisual Ġrepresentations , Ġrespectively . ĠFor Ġthe Ġbi L ST M Ġcomponent , Ġthe Ġdocuments Ġare Ġpre process ed Ġas Ġdescribed Ġin ĠB IB REF 12 Ġ, Ġwhere Ġan Ġarticle Ġis Ġdivided Ġinto Ġsentences Ġand Ġtoken ized Ġusing ĠNL T K ĠB IB REF 38 Ġ. ĠWords Ġappearing Ġmore Ġthan Ġ20 Ġtimes Ġare Ġretained Ġwhen Ġbuilding Ġthe Ġvocabulary . ĠAll Ġother Ġwords Ġare Ġreplaced Ġby Ġthe Ġspecial ĠUN K Ġtoken . ĠWe Ġuse Ġthe Ġpre - trained ĠGl o Ve ĠB IB REF 39 Ġ50 - dimensional Ġword Ġembed d ings Ġto Ġrepresent Ġwords . ĠFor Ġwords Ġnot Ġin ĠGl o Ve , Ġword Ġembed d ings Ġare Ġrandomly Ġinitialized Ġbased Ġon Ġsampling Ġfrom Ġa Ġuniform Ġdistribution Ġ$ U (- 1 , Ġ1 ) $ Ġ. ĠAll Ġword Ġembed d ings Ġare Ġupdated Ġin Ġthe Ġtraining Ġprocess . ĠWe Ġset Ġthe ĠL ST M Ġhidden Ġlayer Ġsize Ġto Ġ256 . ĠThe Ġconc aten ation Ġof Ġthe Ġforward Ġand Ġbackward ĠL ST Ms Ġthus Ġgives Ġus Ġ512 Ġdimensions Ġfor Ġthe Ġdocument Ġembed ding . ĠA Ġdrop out Ġlayer Ġis Ġapplied Ġat Ġthe Ġsentence Ġand Ġdocument Ġlevel , Ġrespectively , Ġwith Ġa Ġprobability Ġof Ġ0 . 5 . </s> For ĠIn ception , Ġwe Ġadopt Ġdata Ġaug mentation Ġtechniques Ġin Ġthe Ġtraining Ġwith Ġa ĠâĢ ľ ne arest âĢ Ŀ Ġfilling Ġmode , Ġa Ġzoom Ġrange Ġof Ġ0 . 1 , Ġa Ġwidth Ġshift Ġrange Ġof Ġ0 . 1 , Ġand Ġa Ġheight Ġshift Ġrange Ġof Ġ0 . 1 . ĠAs Ġthe Ġoriginal Ġscreenshots Ġhave Ġthe Ġsize Ġof Ġ1 , 000 Ġ$\ times Ġ2 $ Ġ, 000 Ġpixels , Ġthey Ġare Ġres ized Ġto Ġ500 Ġ$\ times Ġ$ Ġ500 Ġto Ġfeed Ġinto ĠIn ception , Ġwhere Ġthe Ġinput Ġshape Ġis Ġ( 500 , Ġ500 , Ġ3 ). ĠA Ġdrop out Ġlayer Ġis Ġapplied Ġwith Ġa Ġprobability Ġof Ġ0 . 5 . ĠThen , Ġa ĠGlobal Average Pool ing 2 D Ġlayer Ġis Ġapplied , Ġwhich Ġproduces Ġa Ġ2 , 048 Ġdimensional Ġrepresentation . </s> For Ġthe ĠJoint Ġmodel , Ġwe Ġget Ġa Ġrepresentation Ġof Ġ2 , 560 Ġdimensions Ġby Ġconc aten ating Ġthe Ġ512 Ġdimensional Ġrepresentation Ġfrom Ġthe Ġbi L ST M Ġwith Ġthe Ġ2 , 048 Ġdimensional Ġrepresentation Ġfrom ĠIn ception . ĠThe Ġdrop out Ġlayer Ġis Ġapplied Ġto Ġthe Ġtwo Ġcomponents Ġwith Ġa Ġprobability Ġof Ġ0 . 5 . ĠFor Ġbi L ST M , Ġwe Ġuse Ġa Ġmini - batch Ġsize Ġof Ġ128 Ġand Ġa Ġlearning Ġrate Ġof Ġ0 . 001 . ĠFor Ġboth ĠIn ception Ġand Ġjoint Ġmodel , Ġwe Ġuse Ġa Ġmini - batch Ġsize Ġof Ġ16 Ġand Ġa Ġlearning Ġrate Ġof Ġ0 . 0001 . ĠAll Ġhyper - param eters Ġwere Ġset Ġempir ically Ġover Ġthe Ġdevelopment Ġdata , Ġand Ġthe Ġmodels Ġwere Ġoptimized Ġusing Ġthe ĠAdam Ġoptim izer ĠB IB REF 40 Ġ. </s> In Ġthe Ġtraining Ġphase , Ġthe Ġweights Ġin ĠIn ception Ġare Ġinitialized Ġby Ġparameters Ġpret rained Ġon ĠImage Net , Ġand Ġthe Ġweights Ġin Ġbi L ST M Ġare Ġrandomly Ġinitialized Ġ( except Ġfor Ġthe Ġword Ġembed d ings ). ĠWe Ġtrain Ġeach Ġmodel Ġfor Ġ50 Ġepoch s . ĠHowever , Ġto Ġprevent Ġover fitting , Ġwe Ġadopt Ġearly Ġstopping , Ġwhere Ġwe Ġstop Ġtraining Ġthe Ġmodel Ġif Ġthe Ġperformance Ġon Ġthe Ġdevelopment Ġset Ġdoes Ġnot Ġimprove Ġfor Ġ20 Ġepoch s . ĠFor Ġevaluation , Ġwe Ġuse Ġ( micro -) acc uracy , Ġfollowing Ġprevious Ġstudies ĠB IB REF 5 Ġ, ĠB IB REF 2 Ġ. </s> Bas eline ĠAppro aches </s> We Ġcompare Ġour Ġmodels Ġagainst Ġthe Ġfollowing Ġfive Ġbas elines : </s> Major ity : Ġthe Ġmodel Ġlabels Ġall Ġtest Ġsamples Ġwith Ġthe Ġmajority Ġclass Ġof Ġthe Ġtraining Ġdata . </s> Bench mark : Ġa Ġbenchmark Ġmethod Ġfrom Ġthe Ġliterature . ĠIn Ġthe Ġcase Ġof ĠWikipedia , Ġthis Ġis ĠB IB REF 5 Ġ, Ġwho Ġuse Ġstructural Ġfeatures Ġand Ġread ability Ġscores Ġas Ġfeatures Ġto Ġbuild Ġa Ġrandom Ġforest Ġclass ifier ; Ġfor Ġar X iv , Ġthis Ġis ĠB IB REF 2 Ġ, Ġwho Ġuse Ġhand - crafted Ġfeatures , Ġsuch Ġas Ġthe Ġnumber Ġof Ġreferences Ġand ĠTF - ID F Ġweighted Ġbag - of - words Ġin Ġabstract , Ġto Ġbuild Ġa Ġclass ifier Ġbased Ġon Ġthe Ġbest Ġof Ġlog istic Ġregression , Ġmulti - layer Ġperception , Ġand ĠAda Boost . </s> Doc 2 V ec : Ġdoc 2 vec ĠB IB REF 11 Ġto Ġlearn Ġdocument Ġembed d ings Ġwith Ġa Ġdimension Ġof Ġ500 , Ġand Ġa Ġ4 - layer Ġfeed - forward Ġclassification Ġmodel Ġon Ġtop Ġof Ġthis , Ġwith Ġ2000 , Ġ1000 , Ġ500 , Ġand Ġ200 Ġdimensions , Ġrespectively . </s> bi L ST M : Ġfirst Ġderive Ġa Ġsentence Ġrepresentation Ġby Ġaveraging Ġacross Ġwords Ġin Ġa Ġsentence , Ġthen Ġfeed Ġthe Ġsentence Ġrepresentation Ġinto Ġa Ġbi L ST M Ġand Ġa Ġmax pool ing Ġlayer Ġover Ġoutput Ġsequence Ġto Ġlearn Ġa Ġdocument Ġlevel Ġrepresentation Ġwith Ġa Ġdimension Ġof Ġ512 , Ġwhich Ġis Ġused Ġto Ġpredict Ġdocument Ġquality . </s> In ception Ġ$ _{ \ text { fixed }} $ Ġ: Ġthe Ġfrozen ĠIn ception Ġmodel , Ġwhere Ġonly Ġparameters Ġin Ġthe Ġlast Ġlayer Ġare Ġfine - tun ed Ġduring Ġtraining . </s> The Ġhyper - param eters Ġof ĠBench mark , ĠDoc 2 V ec , Ġand Ġbi L ST M Ġare Ġbased Ġon Ġthe Ġcorresponding Ġpapers Ġexcept Ġthat : Ġ( 1 ) Ġwe Ġfine - t une Ġthe Ġfeed Ġforward Ġlayer Ġof ĠDoc 2 V ec Ġon Ġthe Ġdevelopment Ġset Ġand Ġtrain Ġthe Ġmodel Ġ300 Ġepoch s Ġon ĠWikipedia Ġand Ġ50 Ġepoch s Ġon Ġar X iv ; Ġ( 2 ) Ġwe Ġdo Ġnot Ġuse Ġhand - crafted Ġfeatures Ġfor Ġbi L ST M Ġas Ġwe Ġwant Ġthe Ġbas elines Ġto Ġbe Ġcomparable Ġto Ġour Ġmodels , Ġand Ġthe Ġmain Ġfocus Ġof Ġthis Ġpaper Ġis Ġnot Ġto Ġexplore Ġthe Ġeffects Ġof Ġhand - crafted Ġfeatures Ġ( e . g ., Ġsee ĠB IB REF 12 Ġ). </s> Exper imental ĠResults </s> Table Ġ3 Ġshows Ġthe Ġperformance Ġof Ġthe Ġdifferent Ġmodels Ġover Ġour Ġtwo Ġdatasets , Ġin Ġthe Ġform Ġof Ġthe Ġaverage Ġaccuracy Ġon Ġthe Ġtest Ġset Ġ( along Ġwith Ġthe Ġstandard Ġdeviation ) Ġover Ġ10 Ġruns , Ġwith Ġdifferent Ġrandom Ġinitial izations . </s> On ĠWikipedia , Ġwe Ġobserve Ġthat Ġthe Ġperformance Ġof Ġbi L ST M , ĠIn ception , Ġand ĠJoint Ġis Ġmuch Ġbetter Ġthan Ġthat Ġof Ġall Ġfour Ġbas elines . ĠIn ception Ġachieves Ġ2 . 9 % Ġhigher Ġaccuracy Ġthan Ġbi L ST M . ĠThe Ġperformance Ġof ĠJoint Ġachieves Ġan Ġaccuracy Ġof Ġ59 . 4 %, Ġwhich Ġis Ġ5 . 3 % Ġhigher Ġthan Ġusing Ġtextual Ġfeatures Ġalone Ġ( bi L ST M ) Ġand Ġ2 . 4 % Ġhigher Ġthan Ġusing Ġvisual Ġfeatures Ġalone Ġ( In ception ). ĠBased Ġon Ġa Ġone - tailed ĠWil co xon Ġsigned - rank Ġtest , Ġthe Ġperformance Ġof ĠJoint Ġis Ġstatistically Ġsignificant Ġ( Ġ$ p < 0 . 05 $ Ġ). ĠThis Ġshows Ġthat Ġthe Ġtextual Ġand Ġvisual Ġfeatures Ġcomplement Ġeach Ġother , Ġachieving Ġstate - of - the - art Ġresults Ġin Ġcombination . </s> For Ġar X iv , Ġbaseline Ġmethods ĠMajority , ĠBench mark , Ġand ĠIn ception Ġ$ _{ \ text { fixed }} $ Ġoutper form Ġbi L ST M Ġover Ġcs . ai , Ġin Ġlarge Ġpart Ġbecause Ġof Ġthe Ġclass Ġimbalance Ġin Ġthis Ġdataset Ġ( 90 % Ġof Ġpapers Ġare Ġrejected ). ĠSurprisingly , ĠIn ception Ġ$ _{ \ text { fixed }} $ Ġis Ġbetter Ġthan ĠMajority Ġand ĠBench mark Ġover Ġthe Ġar X iv Ġcs . l g Ġsubset , Ġwhich Ġver ifies Ġthe Ġusefulness Ġof Ġvisual Ġfeatures , Ġeven Ġwhen Ġonly Ġthe Ġlast Ġlayer Ġis Ġfine - tun ed . ĠTable Ġ3 Ġalso Ġshows Ġthat ĠIn ception Ġand Ġbi L ST M Ġachieve Ġsimilar Ġperformance Ġon Ġar X iv , Ġshowing Ġthat Ġtextual Ġand Ġvisual Ġrepresentations Ġare Ġequally Ġdiscrim in ative : ĠIn ception Ġand Ġbi L ST M Ġare Ġindistinguishable Ġover Ġcs . cl ; Ġbi L ST M Ġachieves Ġ1 . 8 % Ġhigher Ġaccuracy Ġover Ġcs . l g , Ġwhile ĠIn ception Ġachieves Ġ1 . 3 % Ġhigher Ġaccuracy Ġover Ġcs . ai . ĠOnce Ġagain , Ġthe ĠJoint Ġmodel Ġachieves Ġthe Ġhighest Ġaccuracy Ġon Ġcs . ai Ġand Ġcs . cl Ġby Ġcombining Ġtextual Ġand Ġvisual Ġrepresentations Ġ( at Ġa Ġlevel Ġof Ġstatistical Ġsignificance Ġfor Ġcs . ai ). ĠThis , Ġagain , Ġconfirms Ġthat Ġtextual Ġand Ġvisual Ġfeatures Ġcomplement Ġeach Ġother , Ġand Ġtogether Ġthey Ġachieve Ġstate - of - the - art Ġresults . ĠOn Ġar X iv Ġcs . l g , ĠJoint Ġachieves Ġa Ġ0 . 6 % Ġhigher Ġaccuracy Ġthan ĠIn ception Ġby Ġcombining Ġvisual Ġfeatures Ġand Ġtextual Ġfeatures , Ġbut Ġbi L ST M Ġachieves Ġthe Ġhighest Ġaccuracy . ĠOne Ġcharacteristic Ġof Ġcs . l g Ġdocuments Ġis Ġthat Ġthey Ġtend Ġto Ġcontain Ġmore Ġequations Ġthan Ġthe Ġother Ġtwo Ġar X iv Ġdatasets , Ġand Ġpreliminary Ġanalysis Ġsuggests Ġthat Ġthe Ġbi L ST M Ġis Ġpicking Ġup Ġon Ġa Ġcorrelation Ġbetween Ġthe Ġvolume / style Ġof Ġmathematical Ġpresentation Ġand Ġthe Ġquality Ġof Ġthe Ġdocument . </s> Analysis </s> In Ġthis Ġsection , Ġwe Ġfirst Ġanalyze Ġthe Ġperformance Ġof ĠIn ception Ġand ĠJoint . ĠWe Ġalso Ġanalyze Ġthe Ġperformance Ġof Ġdifferent Ġmodels Ġon Ġdifferent Ġquality Ġclasses . ĠThe Ġhigh - level Ġrepresentations Ġlearned Ġby Ġdifferent Ġmodels Ġare Ġalso Ġvisual ized Ġand Ġdiscussed . ĠAs Ġthe ĠWikipedia Ġtest Ġset Ġis Ġlarger Ġand Ġmore Ġbalanced Ġthan Ġthat Ġof Ġar X iv , Ġour Ġanalysis Ġwill Ġfocus Ġon ĠWikipedia . </s> In ception </s> To Ġbetter Ġunderstand Ġthe Ġperformance Ġof ĠIn ception , Ġwe Ġgenerated Ġthe Ġgradient - based Ġclass Ġactivation Ġmap ĠB IB REF 41 Ġ, Ġby Ġmaximizing Ġthe Ġoutputs Ġof Ġeach Ġclass Ġin Ġthe Ġpen ultimate Ġlayer , Ġas Ġshown Ġin ĠFigure Ġ3 Ġ. ĠFrom ĠFigure Ġ3 Ġand ĠFigure Ġ3 Ġ, Ġwe Ġcan Ġsee Ġthat ĠIn ception Ġidentifies Ġthe Ġtwo Ġmost Ġimportant Ġregions Ġ( one Ġat Ġthe Ġtop Ġcorresponding Ġto Ġthe Ġtable Ġof Ġcontents , Ġand Ġthe Ġother Ġat Ġthe Ġbottom , Ġcapturing Ġboth Ġdocument Ġlength Ġand Ġreferences ) Ġthat Ġcontribute Ġto Ġthe ĠFA Ġclass Ġprediction , Ġand Ġa Ġregion Ġin Ġthe Ġupper Ġhalf Ġof Ġthe Ġimage Ġthat Ġcontributes Ġto Ġthe ĠGA Ġclass Ġprediction Ġ( capt uring Ġthe Ġlength Ġof Ġthe Ġarticle Ġbody ). ĠFrom ĠFigure Ġ3 Ġand ĠFigure Ġ3 Ġ, Ġwe Ġcan Ġsee Ġthat Ġthe Ġmost Ġimportant Ġregions Ġin Ġterms Ġof ĠB Ġand ĠC Ġclass Ġprediction Ġcapture Ġimages Ġ( down Ġthe Ġleft Ġand Ġright Ġof Ġthe Ġpage , Ġin Ġthe Ġcase Ġof ĠB Ġand ĠC ), Ġand Ġdocument Ġlength / ref erences . ĠFrom ĠFigure Ġ3 Ġand ĠFigure Ġ3 Ġ, Ġwe Ġcan Ġsee Ġthat ĠIn ception Ġfinds Ġthat Ġimages Ġin Ġthe Ġtop Ġright Ġcorner Ġare Ġthe Ġstrongest Ġpredictor Ġof ĠStart Ġclass Ġprediction , Ġand Ġ( the Ġlack Ġof ) Ġimages / the Ġlink Ġbar Ġdown Ġthe Ġleft Ġside Ġof Ġthe Ġdocument Ġare Ġthe Ġmost Ġimportant Ġfor ĠStub Ġclass Ġprediction . </s> J oint </s> Table Ġ4 Ġshows Ġthe Ġconfusion Ġmatrix Ġof ĠJoint Ġon ĠWikipedia . ĠWe Ġcan Ġsee Ġthat Ġmore Ġthan Ġ50 % Ġof Ġdocuments Ġfor Ġeach Ġquality Ġclass Ġare Ġcorrectly Ġclassified , Ġexcept Ġfor Ġthe ĠC Ġclass Ġwhere Ġmore Ġdocuments Ġare Ġmis classified Ġinto ĠB . ĠAnalysis Ġshows Ġthat Ġwhen Ġmis classified , Ġdocuments Ġare Ġusually Ġmis classified Ġinto Ġadjacent Ġquality Ġclasses , Ġwhich Ġcan Ġbe Ġexplained Ġby Ġthe ĠWikipedia Ġgrading Ġscheme , Ġwhere Ġthe Ġcriteria Ġfor Ġadjacent Ġquality Ġclasses Ġare Ġmore Ġsimilar . </s> We Ġalso Ġprovide Ġa Ġbreakdown Ġof Ġprecision Ġ( âĢ ľ Ġ$\ math cal Ġ{ P } $ ĠâĢ Ŀ ), Ġrecall Ġ( âĢ ľ Ġ$\ math cal Ġ{ R } $ ĠâĢ Ŀ ), Ġand ĠF 1 Ġscore Ġ( âĢ ľ Ġ$\ math cal Ġ{ F } _{ \ beta Ġ= 1 } $ ĠâĢ Ŀ ) Ġfor Ġbi L ST M , ĠIn ception , Ġand ĠJoint Ġacross Ġthe Ġquality Ġclasses Ġin ĠTable Ġ5 Ġ. ĠWe Ġcan Ġsee Ġthat ĠJoint Ġachieves Ġthe Ġhighest Ġaccuracy Ġin Ġ11 Ġout Ġof Ġ18 Ġcases . ĠIt Ġis Ġalso Ġworth Ġnoting Ġthat Ġall Ġmodels Ġachieve Ġhigher Ġscores Ġfor ĠFA , ĠGA , Ġand ĠStub Ġarticles Ġthan ĠB , ĠC Ġand ĠStart Ġarticles . ĠThis Ġcan Ġbe Ġexplained Ġin Ġpart Ġby Ġthe Ġfact Ġthat ĠFA Ġand ĠGA Ġarticles Ġmust Ġpass Ġan Ġofficial Ġreview Ġbased Ġon Ġstructured Ġcriteria , Ġand Ġin Ġpart Ġby Ġthe Ġfact Ġthat ĠStub Ġarticles Ġare Ġusually Ġvery Ġshort , Ġwhich Ġis Ġdiscrim in ative Ġfor ĠIn ception , Ġand ĠJoint . ĠAll Ġmodels Ġperform Ġworst Ġon Ġthe ĠB Ġand ĠC Ġquality Ġclasses . ĠIt Ġis Ġdifficult Ġto Ġdifferentiate ĠB Ġarticles Ġfrom ĠC Ġarticles Ġeven Ġfor ĠWikipedia Ġcontributors . ĠAs Ġevidence Ġof Ġthis , Ġwhen Ġwe Ġcrawled Ġa Ġnew Ġdataset Ġincluding Ġtalk Ġpages Ġwith Ġquality Ġclass Ġvotes Ġfrom ĠWikipedia Ġcontributors , Ġwe Ġfound Ġthat Ġamong Ġarticles Ġwith Ġthree Ġor Ġmore Ġquality Ġlabels , Ġover Ġ20 % Ġpercent Ġof ĠB Ġand ĠC Ġarticles Ġhave Ġinconsistent Ġvotes Ġfrom ĠWikipedia Ġcontributors , Ġwhereas Ġfor ĠFA Ġand ĠGA Ġarticles Ġthe Ġnumber Ġis Ġonly Ġ0 . 7 %. </s> We Ġfurther Ġvisualize Ġthe Ġlearned Ġdocument Ġrepresentations Ġof Ġbi L ST M , ĠIn ception , Ġand ĠJoint Ġin Ġthe Ġform Ġof Ġa Ġt - S NE Ġplot ĠB IB REF 42 Ġin ĠFigure Ġ4 Ġ. ĠThe Ġdegree Ġof Ġseparation Ġbetween ĠStart Ġand ĠStub Ġachieved Ġby ĠIn ception Ġis Ġmuch Ġgreater Ġthan Ġfor Ġbi L ST M , Ġwith Ġthe Ġseparation Ġbetween ĠStart Ġand ĠStub Ġachieved Ġby ĠJoint Ġbeing Ġthe Ġcle arest Ġamong Ġthe Ġthree Ġmodels . ĠIn ception Ġand ĠJoint Ġare Ġbetter Ġthan Ġbi L ST M Ġat Ġseparating ĠStart Ġand ĠC . ĠJoint Ġachieves Ġslightly Ġbetter Ġperformance Ġthan ĠIn ception Ġin Ġseparating ĠGA Ġand ĠFA . ĠWe Ġcan Ġalso Ġsee Ġthat Ġit Ġis Ġdifficult Ġfor Ġall Ġmodels Ġto Ġseparate ĠB Ġand ĠC , Ġwhich Ġis Ġconsistent Ġwith Ġthe Ġfindings Ġof ĠTables Ġ4 Ġand Ġ5 Ġ. </s> Con clusions </s> We Ġproposed Ġto Ġuse Ġvisual Ġrender ings Ġof Ġdocuments Ġto Ġcapture Ġimplicit Ġdocument Ġquality Ġindicators , Ġsuch Ġas Ġfont Ġchoices , Ġimages , Ġand Ġvisual Ġlayout , Ġwhich Ġare Ġnot Ġcaptured Ġin Ġtextual Ġcontent . ĠWe Ġapplied Ġneural Ġnetwork Ġmodels Ġto Ġcapture Ġvisual Ġfeatures Ġgiven Ġvisual Ġrender ings Ġof Ġdocuments . ĠExperimental Ġresults Ġshow Ġthat Ġwe Ġachieve Ġa Ġ2 . 9 % Ġhigher Ġaccuracy Ġthan Ġstate - of - the - art Ġapproaches Ġbased Ġon Ġtextual Ġfeatures Ġover ĠWikipedia , Ġand Ġperformance Ġcompetitive Ġwith Ġor Ġsurpass ing Ġstate - of - the - art Ġapproaches Ġover Ġar X iv . ĠWe Ġfurther Ġproposed Ġa Ġjoint Ġmodel , Ġcombining Ġtextual Ġand Ġvisual Ġrepresentations , Ġto Ġpredict Ġthe Ġquality Ġof Ġa Ġdocument . ĠExperimental Ġresults Ġshow Ġthat Ġour Ġjoint Ġmodel Ġoutper forms Ġthe Ġvisual - only Ġmodel Ġin Ġall Ġcases , Ġand Ġthe Ġtext - only Ġmodel Ġon ĠWikipedia Ġand Ġtwo Ġsubs ets Ġof Ġar X iv . ĠThese Ġresults Ġunder line Ġthe Ġfeasibility Ġof Ġassessing Ġdocument Ġquality Ġvia Ġvisual Ġfeatures , Ġand Ġthe Ġcomplement arity Ġof Ġvisual Ġand Ġtextual Ġdocument Ġrepresentations Ġfor Ġquality Ġassessment .