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dc.contributor.advisorLemon, Oliveren
dc.contributor.authorJanarthanam, Srinivasan Chandrasekaranen
dc.date.accessioned2011-08-01T12:53:53Z
dc.date.available2011-08-01T12:53:53Z
dc.date.issued2011-06-30
dc.identifier.urihttp://hdl.handle.net/1842/5033
dc.description.abstractWe address the problem of dynamic user modelling for referring expression generation in spoken dialogue systems, i.e how a spoken dialogue system should choose referring expressions to refer to domain entities to users with different levels of domain expertise, whose domain knowledge is initially unknown to the system. We approach this problem using a statistical planning framework: Reinforcement Learning techniques in Markov Decision Processes (MDP). We present a new reinforcement learning framework to learn user modelling strategies for adaptive referring expression generation (REG) in resource scarce domains (i.e. where no large corpus exists for learning). As a part of the framework, we present novel user simulation models that are sensitive to the referring expressions used by the system and are able to simulate users with different levels of domain knowledge. Such models are shown to simulate real user behaviour more closely than baseline user simulation models. In contrast to previous approaches to user adaptive systems, we do not assume that the user’s domain knowledge is available to the system before the conversation starts. We show that using a small corpus of non-adaptive dialogues it is possible to learn an adaptive user modelling policy in resource scarce domains using our framework. We also show that the learned user modelling strategies performed better in terms of adaptation than hand-coded baselines policies on both simulated and real users. With real users, the learned policy produced around 20% increase in adaptation in comparison to the best performing hand-coded adaptive baseline. We also show that adaptation to user’s domain knowledge results in improving task success (99.47% for learned policy vs 84.7% for hand-coded baseline) and reducing dialogue time of the conversation (11% relative difference). This is because users found it easier to identify domain objects when the system used adaptive referring expressions during the conversations.en
dc.contributor.sponsorBritish Councilen
dc.contributor.sponsorCLASSiC project (FP7)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionSrinivasan Janarthanam and Oliver Lemon. 2010. Learning Adaptive Referring Expressions Generation Policies for Spoken Dialogue Systems. In Krahmer, E., Theune, M., eds.: Empirical Methods in Natural Language Generation. Volume 5980 of Lecture Notes in Computer Science. Springer, Berlin / Heidelberg 2010en
dc.relation.hasversionSrinivasan Janarthanam and Oliver Lemon. 2010c. Adaptive Referring Expression Generation in Spoken Dialogue Systems: Evaluation with Real Users. In proceedings of the 11th Annual SIGDial Conference on Discourse and Dialogue, Tokyo.en
dc.relation.hasversionSrinivasan Janarthanam and Oliver Lemon. 2010b. Learning to Adapt to Unknown Users: Referring Expressions Generation in Spoken Dialogue Systems. In proceedings of the 48th Annual Conference of the Association for Computational Linguistics (ACL), Uppsala.en
dc.relation.hasversionOliver Lemon, Srinivasan Janarthanam and Verena Rieser. 2010a. Generation under uncertainty: Challenge paper. In proceedings of the 6th International Natural Language Generation Conference (INLG), Dublin.en
dc.relation.hasversionSrinivasan Janarthanam and Oliver Lemon. 2009e. A Two-tier User Simulation Model for Reinforcement Learning of Adaptive Referring Expression Generation Policies. In proceedings of the 10th Annual SIGDial Conference on Discourse and Dialogue, London.en
dc.subjectreinforcement learningen
dc.subjectadaptive spoken dialogue systemsen
dc.titleLearning user modelling strategies for adaptive referring expression generation in spoken dialogue systemsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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