Abstract
Emotion Recognition in Conversations (ERC) is the task of identifying the emotions of utterances from speakers in a conversation, which is beneficial to a number of applications, including opinion mining over conversations, developing empathetic dialogue systems, and so on. Many approaches have been proposed to handle this problem in recent years. However, most existing approaches either focus on using RNN-based models to simulate temporal information change in the conversation or graph-based models to take the relationships between the utterances of the speakers into account. In this paper, we propose a temporal and relational graph attention network, named DialogueTRGAT, to combine the strengths of RNN-based models and graph-based models. DialogueTRGAT can better model the intrinsic structure and information flow within a conversation for better emotion recognition. We conduct experiments on two benchmark datasets(IEMOCAP, MELD), and the experimental results demonstrate the great effectiveness of our approach compared with several competitive baselines.
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Notes
- 1.
the speaker’s emotions are influenced by others.
- 2.
emotional inertia of individual speakers.
- 3.
The hidden state of utterance in layer l is equivalent to the representation of utterance in layer l.
References
Busso, C., et al.: Iemocap: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42(4), 335–359 (2008)
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)
Hazarika, D., Poria, S., Mihalcea, R., Cambria, E., Zimmermann, R.: ICON: interactive conversational memory network for multimodal emotion detection. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2594–2604. Association for Computational Linguistics, Brussels, Belgium, Oct-Nov 2018. https://doi.org/10.18653/v1/D18-1280, https://aclanthology.org/D18-1280
Hu, D., Wei, L., Huai, X.: Dialoguecrn: contextual reasoning networks for emotion recognition in conversations. arXiv preprint arXiv:2106.01978 (2021)
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)
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)
Majumder, N.: Mime: mimicking emotions for empathetic response generation. arXiv preprint arXiv:2010.01454 (2020)
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, vol. 33, pp. 6818–6825 (2019)
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)
Shen, W., Wu, S., Yang, Y., Quan, X.: Directed acyclic graph network for conversational emotion recognition. arXiv preprint arXiv:2105.12907 (2021)
Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Thost, V., Chen, J.: Directed acyclic graph neural networks. arXiv preprint arXiv:2101.07965 (2021)
Zhang, D., Wu, L., Sun, C., Li, S., Zhu, Q., Zhou, G.: Modeling both context-and speaker-sensitive dependence for emotion detection in multi-speaker conversations. In: IJCAI, pp. 5415–5421 (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)
Acknowledgments
The authors would like to thank the anonymous reviewers for the helpful comments. This work was supported by Projects 61876118 under the National Natural Science Foundation of China, the National Key RD Program of China under Grant No.2020AAA0108600 and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Kang, J., Kong, F. (2022). DialogueTRGAT: Temporal and Relational Graph Attention Network for Emotion Recognition in Conversations. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https://doi.org/10.1007/978-3-031-17120-8_36
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