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Novel Methods for Natural Language Generation in Spoken Dialogue Systems
Dušek, Ondřej ; Jurčíček, Filip (advisor) ; Ircing, Pavel (referee) ; Žabokrtský, Zdeněk (referee)
Title: Novel Methods for Natural Language Generation in Spoken Dialogue Systems Author: Ondřej Dušek Department: Institute of Formal and Applied Linguistics Supervisor: Ing. Mgr. Filip Jurčíček, Ph.D., Institute of Formal and Applied Linguistics Abstract: This thesis explores novel approaches to natural language generation (NLG) in spoken dialogue systems (i.e., generating system responses to be presented the user), aiming at simplifying adaptivity of NLG in three respects: domain portability, language portability, and user-adaptive outputs. Our generators improve over state-of-the-art in all of them: First, our gen- erators, which are based on statistical methods (A* search with perceptron ranking and sequence-to-sequence recurrent neural network architectures), can be trained on data without fine-grained semantic alignments, thus simplifying the process of retraining the generator for a new domain in comparison to previous approaches. Second, we enhance the neural-network-based gener- ator so that it takes preceding dialogue context into account (i.e., user's way of speaking), thus producing user-adaptive outputs. Third, we evaluate sev- eral extensions to the neural-network-based generator designed for producing output in morphologically rich languages, showing improvements in Czech generation. In...
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Novel Methods for Natural Language Generation in Spoken Dialogue Systems
Dušek, Ondřej ; Jurčíček, Filip (advisor) ; Ircing, Pavel (referee) ; Žabokrtský, Zdeněk (referee)
Title: Novel Methods for Natural Language Generation in Spoken Dialogue Systems Author: Ondřej Dušek Department: Institute of Formal and Applied Linguistics Supervisor: Ing. Mgr. Filip Jurčíček, Ph.D., Institute of Formal and Applied Linguistics Abstract: This thesis explores novel approaches to natural language generation (NLG) in spoken dialogue systems (i.e., generating system responses to be presented the user), aiming at simplifying adaptivity of NLG in three respects: domain portability, language portability, and user-adaptive outputs. Our generators improve over state-of-the-art in all of them: First, our gen- erators, which are based on statistical methods (A* search with perceptron ranking and sequence-to-sequence recurrent neural network architectures), can be trained on data without fine-grained semantic alignments, thus simplifying the process of retraining the generator for a new domain in comparison to previous approaches. Second, we enhance the neural-network-based gener- ator so that it takes preceding dialogue context into account (i.e., user's way of speaking), thus producing user-adaptive outputs. Third, we evaluate sev- eral extensions to the neural-network-based generator designed for producing output in morphologically rich languages, showing improvements in Czech generation. In...
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Development of an English public transport information dialogue system
Vejman, Martin ; Jurčíček, Filip (advisor) ; Peterek, Nino (referee)
This thesis presents a development of an English spoken dialogue system based on the Alex dialogue system framework. The work describes a component adaptation of the framework for a different domain and language. The system provides public transport information in New York. This work involves creating a statistical model and the deployment of custom Kaldi speech recognizer. Its performance was better in comparison with the Google Speech API. The comparison was based on a subjective user satisfaction acquired by crowdsourcing. Powered by TCPDF (www.tcpdf.org)
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