Abstract
Expressing empathy is a trait in human daily conversation, in which people are willing to give responses containing appropriate emotions and topics on the basis of understanding the interlocutor’s situation. However, empathetic dialogue models trained by data-driven training method tend to generate general responses, which are usually monotonous and difficult to infuse emotions and topics concurrently. To solve this issue, we propose a novel model that generates two sub-responses, namely, emotion feedback and topic discussion, then integrates them to optimize empathetic responses. Specifically, in the sub-response generation stage, we introduce emotion lexicon and commonsense knowledge to make sub-responses focus on emotional words and topic-related words respectively, which drives the sub-responses to be contextually related from different perspectives. Afterward, we utilize cross attention to integrate the global information to optimize the final response. Our model is trained on the pre-trained language model BART. Experimental results show that our method can generate responses involving emotion and topic well, and compared with existing methods, empathy and relevance are improved. Our code is available at https://github.com/outsider-lj/edsgi_bart.
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References
Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., Choi, Y.: COMET: commonsense transformers for automatic knowledge graph construction. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp. 4762–4779 (2019)
Gao, J., Liu, Y., Deng, H., Wang, W., Du, Y.C.J., Xu, R.: Improving empathetic response generation by recognizing emotion cause in conversations. In: Findings of the Association for Computational Linguistics: EMNLP2021, pp. 807–819 (2021)
Hwang, J.D., et al.: COMET-ATOMIC 2020: On symbolic and neural commonsense knowledge graphs. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 6384–6392 (2021)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-Softmax. In: Proceedings of the 5th International Conference on Learning Representations (2017)
Kim, H., Kim, B., Kim, G.: Perspective-taking and pragmatics for generating empathetic responses focused on emotion causes. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2227–2240 (2021)
Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871–7880 (2019)
Li, J., Galley, M., Brockett, C., Gao, J., Dolan, 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 36th AAAI Conference on Artificial Intelligence, AAAI 2022, pp. 10993–11001 (2022)
Li, Y., et al.: Towards an online empathetic chatbot with emotion causes. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2041–2045 (2021)
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S.: DailyDialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of the 8th International Joint Conference on Natural Language Processing, pp. 986–995 (2017)
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 (EMNLPIJCNLP), pp. 121–132 (2019)
Lin, Z., et al.: CAiRE: an end-to-end empathetic chatbot. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 13622–13623 (2020)
Liu, W., et al.: K-BERT: enabling language representation with knowledge graph. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 2901–2908 (2020)
Liu, Y., Maier, W., Minker, W., Ultes, S.: Empathetic dialogue generation with pre-trained RoBERTa-GPT2 and external knowledge. arXiv preprint arXiv:2109.03004 (2021)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Liu, Y., Du, J., Li, X., Xu, R.: Generating empathetic responses by injecting anticipated emotion. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, pp. 7403–7407 (2021)
Majumder, N., et al.: MIME: MIMicking emotions for empathetic response generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 8968–8979 (2020)
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, pp. 174–184 (2018)
Mohammad, S.: Word affect intensities. In: Proceedings of the 11th International Conference on Language Resources and Evaluation (2018)
Papineni, K., Roukos, S., Ward, T., Zhu, W.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. Mach. Learn. Res. 21, 1–67 (2020)
Rashkin, H., Smith, E.M., Li, M., Boureau, Y.L.: Towards empathetic opendomain 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 36th AAAI Conference on Artificial Intelligence, AAAI 2022, pp. 11229–11237. AAAI Press (2022)
Shen, L., Zhang, J., Ou, J., Zhao, X., Zhou, J.: Constructing emotional consensus and utilizing unpaired data for empathetic dialogue generation. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3124–3134 (2021)
Shin, J., Xu, P., Madotto, A., Fung, P.: Generating empathetic responses by looking ahead the user’s sentiment. In: Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7989–7993 (2020)
Singer, T., Lamm, C.: The social neuroscience of empathy. Annal. New York Acad. Sci. 1156, 81–96 (2010)
Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of the 31th AAAI Conference on Artificial Intelligence, pp. 4444–4451 (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, Y.H., Hsu, J.H., Wu, C.H., Yang, T.H.: Transformer-based empathetic response generation using dialogue situation and advanced-level definition of empathy. In: Proceedings of the 12th International Symposium on Chinese Spoken Language Processing, pp. 1–5 (2021)
Welivita, A., Xie, Y., Pu, P.: A large-scale dataset for empathetic response generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1251–1264 (2021)
Xia, Y., et al.: Deliberation networks: sequence generation beyond one-pass decoding. In: Proceedings of Advances in Neural Information Processing Systems 30, pp. 1784–1794 (2017)
Zandie, R., Mahoor, M.H.: Emptransfo: A multi-head transformer architecture for creating empathetic dialog systems. In: Proceedings of the 33th International Florida Artificial Intelligence Research Society Conference, pp. 276–281 (2020)
Zaranis, E., Paraskevopoulos, G., Katsamanis, A., Potamianos, A.: EmpBot: A t5-based empathetic chatbot focusing on sentiments. arXiv preprint arXiv:2111.00310 (2021)
Zhang, Y., et al.: DIALOGPT : large-scale generative pre-training for conversational response generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL 2020, pp. 270–278 (2020)
Zhong, P., Zhang, C., Wang, H., Liu, Y., Miao, C.: Towards persona-based empathetic conversational models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 6556–6566 (2020)
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This work is supported by the National Natural Science Foundation of China (61672144, 61872072).
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Li, J., Han, D., Feng, S., Zhang, Y. (2023). Optimizing Empathetic Response by Generating and Integrating Emotion Feedback and Topic Discussion. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13945. Springer, Cham. https://doi.org/10.1007/978-3-031-30675-4_46
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DOI: https://doi.org/10.1007/978-3-031-30675-4_46
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