Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g., relations. In this work, we propose a novel dialogue model that is centred around entities and is able to model relations as well as multiple entities of the same type. We demonstrate in a prototype implementation benefits of relation modelling on the dialogue level and show that a trained policy using these relations outperforms the multi-domain baseline. Furthermore, we show that by modelling the relations on the dialogue level, the system is capable of processing relations present in the user input and even learns to address them in the system response.
The proposed CEDM achieves this by modelling state information about conversational entities instead of domains. More precisely, it models separate states about the objects (e.g., the hotel or restaurant) and the relations. Previous work on dialogue modelling already incorporated the idea of objects or entities to be the principal component of the dialogue state @cite_12 @cite_13 @cite_16 @cite_22 @cite_10 . However, these dialogue models are not based on statistical dialogue processing where a probability distribution over all dialogue states needs to be modelled and maintained. This additional complexity, though, cannot be incorporated in a straight-forward way into the proposed models. In contrast, the CEDM offers a comprehensive and consistent way of modelling these probabilities by defining and maintaining entity-based states. Work on statistical dialogue state modelling @cite_30 @cite_21 @cite_19 also contain a variant of objects but is still based on the MDDM thus not offering any mechanism to model multiple entities or relations between objects. proposed a belief tracking approach using relational trees. However, they only consider static relations present in the ontology and are not able to handle dynamic relations.
@cite_13 @cite_30 @cite_22 @cite_21 @cite_19 @cite_16 @cite_10 @cite_12
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This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialogue systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes in some detail a form of approximation called the Hidden Information State model which does scale and which can be used to build practical systems. A prototype HIS system for the tourist information domain is evaluated and compared with a baseline MDP system using both user simulations and a live user trial. The results give strong support to the central contention that the POMDP-based framework is both a tractable and powerful approach to building more robust spoken dialogue systems. This paper introduces a new dialogue management framework for goal-directed conversations. A declarative specification defines the domain-specific elements and guides the dialogue manager, which communicates with the knowledge sources to complete the specified goal. The user is viewed as another knowledge source. The dialogue manager finds the next action by a mixture of rule-based reasoning and a simple statistical model. Implementation in the flight-reservation domain demonstrates that the framework enables the developer to easily build a conversational dialogue system. Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model. In this paper we present a plug and play dialogue system for smart environments. The environment description and its state are stored on a domain ontology. This ontology is formed by entities that represent real world contextual information and abstract concepts. This information is complemented with linguistic parts that allow to automatically create a spoken interface for the environment. The spoken interface is based on multiple dialogues, related to every ontology entity with linguistic information. Firstly, the dialogue system creates appropriate grammars for the dialogues. Secondly, it creates the dialogue parts, employing a tree structure. Grammars support the recognition process and the dialogue tree supports the interpretation and generation processes. The system is being tested with a prototype formed by a living room. Users may interact with and modify the physical state of this living room environment by means of the spoken dialogue interface. Introducing Spoken Dialogue Systems into Intelligent Environments outlines the formalisms of a novel knowledge-driven framework for spoken dialogue management and presents the implementation ofa model-based Adaptive Spoken Dialogue Manager(ASDM) called OwlSpeak. The authors have identified three stakeholders thatpotentially influence the behavior of the ASDM: the user, the SDS, and a complex Intelligent Environment (IE) consisting of various devices, services, and task descriptions. The theoretical foundation of a working ontology-based spoken dialogue description framework, the prototype implementation of the ASDM, and the evaluation activities that are presented as part of this book contribute to the ongoing spoken dialogue research by establishing the fertile ground of model-based adaptive spoken dialogue management. Thismonograph is ideal for advanced undergraduate students, PhD students, and postdocs as well as academic and industrial researchers and developers in speech and multimodal interactive systems. Abstract : This report develops a representation of focus of attention thatcircumscribes discourse contexts within a general representation ofknowledge. Focus of attention is essential to any comprehension processbecause what and how a person understands is strongly influenced bywhere his attention is directed at a given moment. To formalize thenotion of focus, the need for and the use of focus mechanisms areconsidered from the standpoint of building a computer system that canparticipate in a natural language dialogue with a ser, Two ranges offocus, global and immediate, are investigated, and representations forincorporating them in a computer system are developed.The global focus in which an utterance is interpreted is determinedby the total discourse and situational setting of the utterance. Itinfluences what is talked about, how different concepts are introduced,and how concepts are referenced. To encode global focuscomputationally, a representation is developed that highlights thoseitems that are relevant at a given place in a dialogue. The underlyingknowledge representation is segmented into subunits, called focusspaces, that contain those items that are in the focus of attention of adialogue participant during a particular part of the dialogue.Mechanisms are required for updating the focus representation,because, as a dialogue progresses, the objects and actions that arerelevant to the conversation, and therefore in the participants' focusof attention, change. Procedures are described for deciding when andhow to shift focus in task-oriented dialogues, i.e., in dialogues inwhich the participants are cooperating in a shared task. Theseprocedures are guided by a representation of the task being performed.The ability to represent focus of attention in a languageunderstanding system results in a new approach to an important problemin discourse comprehension -- the identification of the referents ofdefinite noun phrases.