Coherence is the quality that gives a text its conceptual unity, making a text a coordinated set of connected parts rather than a random group of sentences (turns, in the case of dialogue). Hence, coherence is an integral property of human communication, necessary for a meaningful discourse both in text and dialogue. As such, coherence can be regarded as a requirement for conversational agents, i.e. machines designed to converse with humans. Though recently there has been a proliferation in the usage and popularity of conversational agents, dialogue coherence is still a relatively neglected area of research, and coherence across multiple turns of a dialogue remains an open challenge for current conversational AI research. As conversational agents progress from being able to handle a single application domain to multiple ones through any domain (open-domain), the range of possible dialogue paths increases, and thus the problem of maintaining multi-turn coherence becomes especially critical.
In this thesis, we investigate two aspects of coherence in dialogue and how they can be used to design modules for an open-domain coherent conversational agent. In particular, our approach focuses on modeling intentional and thematic information patterns of distribution as proxies for a coherent discourse in open-domain dialogue. While for modeling intentional information we employ Dialogue Acts (DA) theory (Bunt, 2009); for modeling thematic information we rely on open-domain entities (Barzilay and Lapata, 2008). We find that DAs and entities play a fundamental role in modelling dialogue coherence both independently and jointly, and that they can be used to model different components of an open-domain conversational agent architecture, such as Spoken Language Understanding, Dialogue Management, Natural Language Generation, and open-domain dialogue evaluation.
The main contributions of this thesis are: (I) we present an open-domain modular conversational agent architecture based on entity and DA structures designed for coherence and engagement; (II) we propose a methodology for training an open-domain DA tagger compliant with the ISO 24617-2 standard (Bunt et al., 2012) combining multiple resources; (III) we propose different models, and a corpus, for predicting open-domain dialogue coherence using DA and entity information trained with weakly supervised techniques, first at the conversation level and then at the turn level; (IV) we present supervised approaches for automatic evaluation of open-domain conversation exploiting DA and entity information, both at the conversation level and at the turn level; (V) we present experiments with Natural Language Generation models that generate text from Meaning Representation structures composed of DAs and slots for an open-domain setting.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/276165 |
| Date | 08 October 2020 |
| Creators | Cervone, Alessandra |
| Contributors | Cervone, Alessandra, Riccardi, Giuseppe |
| Publisher | Università degli studi di Trento, place:Trento |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:152, numberofpages:152 |
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