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Generating Responses Expressing Emotion in an Open-Domain Dialogue System

  • Chenyang Huang
  • Osmar R. ZaïaneEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11551)

Abstract

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.

Keywords

Open-domain dialogue generation Emotion Seq2seq Attention mechanism 

References

  1. 1.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)
  2. 2.
    Bessho, F., Harada, T., Kuniyoshi, Y.: Dialog system using real-time crowdsourcing and Twitter large-scale corpus. In: Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 227–231. Association for Computational Linguistics (2012)Google Scholar
  3. 3.
    Bickmore, T.W., Picard, R.W.: Establishing and maintaining long-term human-computer relationships. ACM Trans. Comput.-Hum. Interact. 12(2), 293–327 (2005).  https://doi.org/10.1145/1067860.1067867CrossRefGoogle Scholar
  4. 4.
    Ekman, P., Friesen, W.V., Ellsworth, P.: Emotion in the Human Face: Guide-Lines for Research and an Integration of Findings. Pergamon, Oxford (1972)Google Scholar
  5. 5.
    Graves, A., Fernández, S., Schmidhuber, J.: Bidirectional LSTM networks for improved phoneme classification and recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 799–804. Springer, Heidelberg (2005).  https://doi.org/10.1007/11550907_126CrossRefGoogle Scholar
  6. 6.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  7. 7.
    Huang, C., Zaiane, O., Trabelsi, A., Dziri, N.: Automatic dialogue generation with expressed emotions. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, vol. 2, pp. 49–54 (2018).  https://doi.org/10.18653/v1/n18-2008
  8. 8.
    Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext.zip: compressing text classification models. arXiv:1612.03651 (2016)
  9. 9.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
  10. 10.
    Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., Dolan, B.: A persona-based neural conversation model. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 994–1003 (2016).  https://doi.org/10.18653/v1/p16-1094
  11. 11.
    Li, J., Monroe, W., Jurafsky, D.: Data distillation for controlling specificity in dialogue generation. arXiv:1702.06703 (2017)
  12. 12.
    Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., Jurafsky, D.: Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 (2016).  https://doi.org/10.18653/v1/d16-1127
  13. 13.
    Li, J., Monroe, W., Shi, T., Ritter, A., Jurafsky, D.: Adversarial learning for neural dialogue generation. arXiv:1701.06547 (2017).  https://doi.org/10.18653/v1/d17-1230
  14. 14.
    Lin, Z., et al.: A structured self-attentive sentence embedding. arXiv:1703.03130 (2017)
  15. 15.
    Lison, P., Tiedemann, J.: OpenSubtitles 2016: extracting large parallel corpora from movie and TV subtitles (2016)Google Scholar
  16. 16.
    Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421 (2015).  https://doi.org/10.18653/v1/d15-1166
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  18. 18.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th ACL and the 4th AFNLP, vol. 2, pp. 1003–1011 (2009).  https://doi.org/10.3115/1690219.1690287
  19. 19.
    Mohammad, S.M.: # emotional tweets. In: Proceedings of the 1st Joint Conference on Lexical and Computational Semantics, Proceedings of the Main Conference and the Shared Task, Proceedings of the 6th International Workshop on Semantic Evaluation, vols. 1, 2, pp. 246–255 (2012)Google Scholar
  20. 20.
    Parrott, W.G.: Emotions in Social Psychology: Essential Readings. Psychology Press, London (2001)Google Scholar
  21. 21.
    Ritter, A., Cherry, C., Dolan, W.B.: Data-driven response generation in social media. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 583–593. Association for Computational Linguistics (2011)Google Scholar
  22. 22.
    Scherer, K.R., Wallbott, H.G.: Evidence for universality and cultural variation of differential emotion response patterning. J. Pers. Soc. Psychol. 66(2), 310 (1994).  https://doi.org/10.1037/0022-3514.67.1.55CrossRefGoogle Scholar
  23. 23.
    Serban, I.V., et al.: A hierarchical latent variable encoder-decoder model for generating dialogues. In: AAAI, pp. 3295–3301 (2017).  https://doi.org/10.21437/interspeech.2017-628
  24. 24.
    Shahraki, A.G., Zaiane, O.R.: Lexical and learning-based emotion mining from text. In: Proceedings of CICLing (2017)Google Scholar
  25. 25.
    Shaver, P., Schwartz, J., Kirson, D., O’Connor, C.: Emotion knowledge: further exploration of a prototype approach. J. Pers. Soc. Psychol. 52(6), 1061 (1987).  https://doi.org/10.1037/0022-3514.52.6.1061CrossRefGoogle Scholar
  26. 26.
    Silva, J., Coheur, L., Mendes, A.C., Wichert, A.: From symbolic to sub-symbolic information in question classification. Artif. Intell. Rev. 35(2), 137–154 (2011).  https://doi.org/10.1007/s10462-010-9188-4CrossRefGoogle Scholar
  27. 27.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  28. 28.
    Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)Google Scholar
  29. 29.
    Vinyals, O., Le, Q.: A neural conversational model. arXiv:1506.05869 (2015)
  30. 30.
    Xu, W., Rudnicky, A.I.: Task-based dialog management using an agenda. In: Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems, vol. 3, pp. 42–47. Association for Computational Linguistics (2000).  https://doi.org/10.3115/1117562.1117571
  31. 31.
    Yadollahi, A., Shahraki, A.G., Zaiane, O.R.: Current state of text sentiment analysis from opinion to emotion mining. ACM Comput. Surv. (CSUR) 50(2), 25 (2017).  https://doi.org/10.1145/3057270CrossRefGoogle Scholar
  32. 32.
    Zhou, H., Huang, M., Zhang, T., Zhu, X., Liu, B.: Emotional chatting machine: emotional conversation generation with internal and external memory. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)Google Scholar
  33. 33.
    Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv:1611.06639 (2016)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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