Abstract
An engaging dialogue system is supposed to generate empathetic responses, which requires a cognitive understanding of users’ situations and an affective perception of their emotions. Most of the existing work only focuses on modeling the latter, while neglecting the importance of the former. Despite some efforts to enhance chatbots’ empathy in both cognition and affection, limited cognition conditions and inaccessible fine-grained information still impair the effectiveness of empathy modeling. To address this issue, we propose a novel fine-grained knowledge-enhanced empathetic dialogue generation model KEEM. We first explore strategies to filter fine-grained commonsense and emotional knowledge and leverage knowledge to construct cognitive and affective context graphs. And we learn corresponding context representations from the two knowledge-enhanced context graphs. Then we encode the raw dialogue context to learn the original cognitive and affective representations and fuse them with the knowledge-enhanced representations in cognition and affection. Finally, we feed the two fused representations into a decoder to produce empathetic replies. Extensive experiments conducted on the benchmark dataset EMPATHETICDIALOGUES verify the effectiveness of our model in comparison with several competitive models.
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References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Cuff, B., Brown, S.J., Taylor, L., Howat, D.J.: Empathy: a review of the concept. Emot. Rev. 8, 144–153 (2014)
Elliott, R., Bohart, A.C., Watson, J.C., Murphy, D.: Therapist empathy and client outcome: an updated meta-analysis. Psychotherapy 55(4), 399 (2018)
Jiang, S., Ren, P., Monz, C., de Rijke, M.: Improving neural response diversity with frequency-aware cross-entropy loss. In: The World Wide Web Conference, pp. 2879–2885 (2019)
Keskin, S.C.: From what isn’t empathy to empathic learning process. Procedia. Soc. Behav. Sci. 116, 4932–4938 (2014)
Li, J., Galley, M., Brockett, C., Gao, J., Dolan, W.B.: A diversity-promoting objective function for neural conversation models. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 110–119 (2016)
Li, Q., Chen, H., Ren, Z., Ren, P., Tu, Z., Chen, Z.: EmpDG: multi-resolution interactive empathetic dialogue generation. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4454–4466 (2020)
Li, Q., Li, P., Ren, Z., Ren, P., Chen, Z.: Knowledge bridging for empathetic dialogue generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 10993–11001 (2022)
Lin, Z., Madotto, A., Shin, J., Xu, P., Fung, P.: Moel: Mixture of empathetic listeners. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 121–132 (2019)
Majumder, N., et al.: Mime: mimicking emotions for empathetic response generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8968–8979 (2020)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Computer Science (2013)
Mohammad, S.: Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (volume 1: Long Papers), pp. 174–184 (2018)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods In Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Rashkin, H., Smith, E.M., Li, M., Boureau, Y.L.: Towards empathetic open-domain conversation models: a new benchmark and dataset. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5370–5381 (2019)
Sabour, S., Zheng, C., Huang, M.: CEM: commonsense-aware empathetic response generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 11229–11237 (2022)
Serban, I.V., Sordoni, A., Bengio, Y., Courville, A., Pineau, J.: Hierarchical neural network generative models for movie dialogues. arXiv preprint arXiv:1507.048087(8), 434–441 (2015)
Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Welivita, A., Pu, P.: A taxonomy of empathetic response intents in human social conversations. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4886–4899 (2020)
Zech, E., Rimé, B.: Is talking about an emotional experience helpful? effects on emotional recovery and perceived benefits. Clin. Psychol. Psychoth. Int. J. Theory Pract. 12(4), 270–287 (2005)
Zhong, P., Wang, D., Miao, C.: Knowledge-enriched transformer for emotion detection in textual conversations. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 165–176 (2019)
Acknowledgements
The authors would like to thank the Associate Editor and anonymous reviewers for their valuable comments and suggestions. This work is funded in part by the National Natural Science Foundation of China under Grants No.62176029. This work also is supported in part by the Chongqing Technology Innovation and Application Development Special under Grants CSTB2022TIAD-KPX0206. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
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Chen, A., Zhong, J., Dai, Q., Wang, C., Li, R. (2023). Fine-Grained Knowledge Enhancement for Empathetic Dialogue Generation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_6
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DOI: https://doi.org/10.1007/978-3-031-46674-8_6
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