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A computational model for automatic generation of domain-specific dialogues using machine learning

Published: 25 September 2017 Publication History

Abstract

Automatic generation of dialogues is a very important component for the human-robot interaction task ; the dialogues generated must guarantee a coherent conversation between human beings and robots. The aim is that the interaction is as natural and effective as possible, considering aspects such as: age, gender, socio-cultural level, socio-economic level, and so on.
This research report presents an overview of a doctoral research work that is intended to be executed during the following three years. We motivate the necessity of researching in automatic generation and evaluation of dialogues in the framework of human-robot interaction, presenting the related work reported in literature, the research objectives and the methodology we are interested in develop. In general, we propose to employ machine learning techniques in a restricted domain of knowledge for generating the human-robot dialogues.

References

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Konstantopoulos, S. (2010). An Embodied Dialogue System with Personality and Emotions. Proceedings of the 2010 Workshop on Companionable Dialogue Systems, ACL 2010, 31--36.
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Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., and Jurafsky, D. (2016). Deep Reinforcement Learning for Dialogue Generation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 1--5 November 2016, 1192--1202.
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Lison, P. (2012). Probabilistic Dialogue Models with Prior Domain Knowledge. Association for Computational Linguistics. Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), Seoul, South Korea, 5--6 Julio 2012, 179--188.
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Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Dolan, B. (2015). A Neural Network Approach to Context-Sensitive Generation of Conversational Responses. Proceedings of NAACL-HLT 2015.
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Su, P., Gasic, M., Mrksic, N., Rojas-Barahona, L., Ultes, S., Vandyke, D., Young S. (2016). Continuously Learning Neural Dialogue Management. arXiv:1606.02689v1 {cs.CL} 8 Jun 2016.
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Wen, T., Gasic, M., Mrksic, N., Su, P., Vandyke D., and Young, S. (2015). Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics. Lisbon, Portugal, 17--21 September 2015, 1711--1721.
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  1. A computational model for automatic generation of domain-specific dialogues using machine learning

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      cover image ACM Other conferences
      Interacción '17: Proceedings of the XVIII International Conference on Human Computer Interaction
      September 2017
      268 pages
      ISBN:9781450352291
      DOI:10.1145/3123818
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      New York, NY, United States

      Publication History

      Published: 25 September 2017

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      Author Tags

      1. automatic dialogue generation
      2. human-robot interaction

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