Abstract
Emotion Recognition in Conversations (ERC) is the task of identifying the emotions of utterances from speakers in a conversation, which is beneficial to many applications. In this paper, we introduce two kinds of external knowledge, i.e., dialogue discourse structure and social commonsense knowledge implied in dialogue to enhance representation and emotional reasoning. The dialogue discourse structure directly reveals the adjacent or long-distance dependencies between utterances and provides prior knowledge for the semantic interaction between utterances. Implicit commonsense knowledge in utterances can serve as emotional inference cues to model deeper inter-utterance emotional interactions. Specifically, we construct a discourse structure and commonsense knowledge enhanced graph structure over the conversation and use graph convolutional networks to incorporate historical context and commonsense knowledge for utterances. Experimental results show that incorporating discourse structure and commonsense knowledge can effectively improve the performance of the model.
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Asher, N., Hunter, J., Morey, M., Benamara, F., Afantenos, S.: Discourse structure and dialogue acts in multiparty dialogue: the STAC corpus. In: 10th International Conference on Language Resources and Evaluation (LREC 2016), pp. 2721–2727 (2016)
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 Annual Meeting of the Association for Computational Linguistics, pp. 4762–4779 (2019)
Busso, C., et al.: IEMOCAP: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42, 335–359 (2008)
Firdaus, M., Singh, G.V., Ekbal, A., Bhattacharyya, P.: Affect-GCN: a multimodal graph convolutional network for multi-emotion with intensity recognition and sentiment analysis in dialogues. Multimed. Tools Appl., 1–22 (2023)
Ghosal, D., Majumder, N., Gelbukh, A., Mihalcea, R., Poria, S.: COSMIC: commonsense knowledge for emotion identification in conversations. arXiv preprint arXiv:2010.02795 (2020)
Ghosal, D., Majumder, N., Poria, S., Chhaya, N., Gelbukh, A.: DialogueGCN: a graph convolutional neural network for emotion recognition in conversation. arXiv preprint arXiv:1908.11540 (2019)
Ishiwatari, T., Yasuda, Y., Miyazaki, T., Goto, J.: Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7360–7370 (2020)
Jiao, W., Yang, H., King, I., Lyu, M.R.: HIGRU: hierarchical gated recurrent units for utterance-level emotion recognition. arXiv preprint arXiv:1904.04446 (2019)
Kang, J., Kong, F.: DialogueTRGAT: temporal and relational graph attention network for emotion recognition in conversations. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds.) NLPCC 2022. LNCS, vol. 13551, pp. 460–472. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17120-8_36
Kumar, A., Dogra, P., Dabas, V.: Emotion analysis of Twitter using opinion mining. In: 2015 Eighth International Conference on Contemporary Computing (IC3), pp. 285–290. IEEE (2015)
Li, J., Lin, Z., Fu, P., Wang, W.: Past, present, and future: conversational emotion recognition through structural modeling of psychological knowledge. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 1204–1214 (2021)
Li, W., Zhu, L., Mao, R., Cambria, E.: SKIER: a symbolic knowledge integrated model for conversational emotion recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence (2023)
Liu, H., Singh, P.: ConceptNet—a practical commonsense reasoning toolkit. BT Technol. J. 22(4), 211–226 (2004)
Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: DialogueRNN: an attentive RNN for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence, no. 01, pp. 6818–6825 (2019)
Peng, S., et al.: Multi-source domain adaptation method for textual emotion classification using deep and broad learning. Knowl. Based Syst. 260, 110173 (2023)
Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., Morency, L.P.: Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 873–883 (2017)
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R.: MELD: a multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508 (2018)
Sap, M., et al.: ATOMIC: an atlas of machine commonsense for if-then reasoning. In: Proceedings of the AAAI Conference on Artificial Intelligence, no. 01, pp. 3027–3035 (2019)
Shen, W., Chen, J., Quan, X., Xie, Z.: DialogXL: all-in-one XLNet for multi-party conversation emotion recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, no. 15, pp. 13789–13797 (2021)
Shen, W., Wu, S., Yang, Y., Quan, X.: Directed acyclic graph network for conversational emotion recognition. arXiv preprint arXiv:2105.12907 (2021)
Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., Sun, Y.: Masked label prediction: unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509 (2020)
Shi, Z., Huang, M.: A deep sequential model for discourse parsing on multi-party dialogues. In: Proceedings of the AAAI Conference on Artificial Intelligence, no. 01, pp. 7007–7014 (2019)
Song, R., Giunchiglia, F., Shi, L., Shen, Q., Xu, H.: SUNet: speaker-utterance interaction graph neural network for emotion recognition in conversations. Eng. Appl. Artif. Intell. 123, 106315 (2023)
Wen, J., Jiang, D., Tu, G., Liu, C., Cambria, E.: Dynamic interactive multiview memory network for emotion recognition in conversation. Inf. Fus. 91, 123–133 (2023)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Zhang, D., Chen, X., Xu, S., Xu, B.: Knowledge aware emotion recognition in textual conversations via multi-task incremental transformer. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4429–4440 (2020)
Zhao, W., Zhao, Y., Lu, X.: CauAIN: causal aware interaction network for emotion recognition in conversations. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI, pp. 4524–4530 (2022)
Zhong, P., Wang, D., Miao, C.: Knowledge-enriched transformer for emotion detection in textual conversations. arXiv preprint arXiv:1909.10681 (2019)
Zhou, L., Gao, J., Li, D., Shum, H.Y.: The design and implementation of Xiaoice, an empathetic social chatbot. Comput. Linguist. 46(1), 53–93 (2020)
Zhu, L., Pergola, G., Gui, L., Zhou, D., He, Y.: Topic-driven and knowledge-aware transformer for dialogue emotion detection. arXiv preprint arXiv:2106.01071 (2021)
Acknowledgements
This work was supported by the Project 62276178 under the National Natural Science Foundation of China, the Key Project 23KJA520012 under the Natural Science Foundation of Jiangsu Higher Education Institutions and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Hao, J., Kong, F., Kang, J. (2024). Enhancing Emotion Recognition in Conversation with Dialogue Discourse Structure and Commonsense Knowledge. In: Huang, DS., Si, Z., Zhang, C. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14878. Springer, Singapore. https://doi.org/10.1007/978-981-97-5672-8_22
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DOI: https://doi.org/10.1007/978-981-97-5672-8_22
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