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Utterance Selection Using Discourse Relation Filter for Chat-oriented Dialogue Systems

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 427))

Abstract

We propose a novel utterance selection method for chat-oriented dialogue systems . Many chat-oriented dialogue systems have huge databases of candidate utterances for utterance generation. However, many of these systems have a critical issue in that they select utterances that are inappropriate to the past conversation due to a limitation in contextual understanding. We solve this problem with our proposed method, which uses a discourse relation to the last utterance when selecting an utterance from candidate utterances. We aim to improve the performance of system utterance selection by preferentially selecting an utterance that has a discourse relation to the last utterance. Experimental results with human subjects showed that our proposed method was more effective than previous utterance selection methods.

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References

  1. Bickmore, T.W., Picard, R.W.: Establishing and maintaining long-term human-computer relationships. ACM Trans. Comput. Hum. Interact. 293–327 (2005)

    Google Scholar 

  2. Ritter, A., Cherry, C., Dolan, W.B.: Data-driven response generation in social media. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP2011), pp. 583–593 (2011)

    Google Scholar 

  3. Higashinaka, R., Imamura, K., Meguro, T., Miyazaki, C., Kobayashi, N., Sugiyama, H., Hirano, T., Makino, T., Matsuo, Y.: Towards an open domain conversational system fully based on natural language processing. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 928–939 (2014)

    Google Scholar 

  4. Banchs, R.E., Li, H.: Iris: a chat-oriented dialogue system based on the vector space model. In: Proceedings of the ACL 2012 System Demonstrations (ACL2012), pp. 37–42 (2012)

    Google Scholar 

  5. Bang, J., Noh, H., Kim, Y., Lee, G.: Example-based chat-oriented dialogue system with personalized long-term memory. In: Proceedings of 2015 International Conference on Big Data and Smart Computing (Big-Comp2015), pp. 238–243 (2015)

    Google Scholar 

  6. Tonelli, S., Riccardi, G., Prasad, R., Joshi, A.: Annotation of discourse relations for conversational spoken dialogs. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), pp. 19–21 (2010)

    Google Scholar 

  7. Afantenos, S., Kow, E., Asher, N., Perret, J.: Discourse parsing for multi-party chat dialogues. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP2015), pp. 928–937 (2015)

    Google Scholar 

  8. Stoyanchev, S., Piwek, P.: The coda system for monologue-to-dialogue generation. In: Proceedings of the 12th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL2011), pp. 335–337 (2011)

    Google Scholar 

  9. Shibata, M., Nishiguchi, T., Tomiura, Y.: Dialog system for open-ended conversation using web documents. Informatica 33, 277–284 (2009)

    Google Scholar 

  10. Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: A Framework for the Analysis of Texts. Information Sciences Institute (ISI/RS- 87-185) (1987)

    Google Scholar 

  11. Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A., Webber, B.: The penn discourse treebank 2.0. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008) (2008)

    Google Scholar 

  12. Marcu, D., Echihabi, A.: An unsupervised approach to recognizing discourse relations. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL 2002) pp. 368–375 (2002)

    Google Scholar 

  13. Lin, Z., Kan, M.Y., Ng, H.T.: Recognizing implicit discourse rela- tions in the penn discourse treebank. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP 2009), pp. 343–351 (2009)

    Google Scholar 

  14. Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Asian Federation of Natural Language Processing (AFNLP 2009), pp. 683–691 (2009)

    Google Scholar 

  15. Wang, X., Li, S., Li, J., Li, W.: Implicit discourse relation recognition by selecting typical training examples. In: Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pp. 2757–2772 (2012)

    Google Scholar 

  16. Biran, O., McKeown, K.: Aggregated word pair features for implicit discourse relation disambiguation. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 69–73 (2013)

    Google Scholar 

  17. Rutherford, A., Xue, N.: Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), pp. 645–654 (2014)

    Google Scholar 

  18. Otsuka, A., Hirano, T., Miyazaki, C., Masumura, R., Higashinaka, R., Makino, T., Matsuo, Y.: Discourse relation recognition by comparing various units of sentence expression with recursive neural network. In: Proceedings of the 28th Pacific Asia Conference on Language, Information, and Computation (PACLIC2015), pp. 63–72 (2015)

    Google Scholar 

  19. Higashinaka, R., Funakoshi, K., Araki, M., Tsukahara, H., Kobayashi, Y., Mizukami, M.: Towards taxonomy of errors in chat-oriented dialogue systems. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL2015), pp. 87–95 (2015)

    Google Scholar 

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Correspondence to Atsushi Otsuka .

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Otsuka, A., Hirano, T., Miyazaki, C., Higashinaka, R., Makino, T., Matsuo, Y. (2017). Utterance Selection Using Discourse Relation Filter for Chat-oriented Dialogue Systems. In: Jokinen, K., Wilcock, G. (eds) Dialogues with Social Robots. Lecture Notes in Electrical Engineering, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-10-2585-3_29

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  • DOI: https://doi.org/10.1007/978-981-10-2585-3_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2584-6

  • Online ISBN: 978-981-10-2585-3

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