Abstract
Emotion Recognition in Conversations (ERC) aims to predict the emotion of each utterance in a given conversation. Existing approaches for the ERC task mainly suffer from two drawbacks: (1) failing to pay enough attention to the emotional impact of the local context; (2) ignoring the effect of the emotional inertia of speakers. To tackle these limitations, we first propose a Hierarchical Multimodal Transformer as our base model, followed by carefully designing a localness-aware attention mechanism and a speaker-aware attention mechanism to respectively capture the impact of the local context and the emotional inertia. Extensive evaluations on a benchmark dataset demonstrate the superiority of our proposed model over existing multimodal methods for ERC.
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Notes
- 1.
Utterance is typically defined as a unit of speech bounded by breathes or pause [10].
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Chen, S.Y., Hsu, C.C., Kuo, C.C., Ku, L.W., et al.: Emotionlines: an emotion corpus of multi-party conversations. arXiv preprint arXiv:1802.08379 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Ekman, P.: An argument for basic emotions. Cogn. Emotion 6(3ā4), 169ā200 (1992)
Eyben, F., Wƶllmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459ā1462 (2010)
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. 2594ā2604 (2018)
Jiao, W., Lyu, M.R., King, I.: Real-time emotion recognition via attention gated hierarchical memory network. arXiv preprint arXiv:1911.09075 (2019)
Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: Dialoguernn: an attentive rnn for emotion detection in conversations. Proc. AAAI Conf. Artif. Intell. 33, 6818ā6825 (2019)
Olson, D.: From utterance to text: the bias of language in speech and writing. Harvard Educ. Rev. 47(3), 257ā281 (1977)
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. 873ā883 (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)
Tsai, Y.H.H., Bai, S., Liang, P.P., Kolter, J.Z., Morency, L.P., Salakhutdinov, R.: Multimodal transformer for unaligned multimodal language sequences. arXiv preprint arXiv:1906.00295 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998ā6008 (2017)
Yang, B., Li, J., Wong, D.F., Chao, L.S., Wang, X., Tu, Z.: Context-aware self-attention networks. Proc. AAAI Conf. Artif. Intell. 33, 387ā394 (2019)
Yang, B., Tu, Z., Wong, D.F., Meng, F., Chao, L.S., Zhang, T.: Modeling localness for self-attention networks. arXiv preprint arXiv:1810.10182 (2018)
Yuan, J., Liberman, M.: Speaker identification on the scotus corpus. J. Acoustical Soc. Am. 123(5), 3878 (2008)
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: See Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 10ā16. IJCAI (2019)
Zhong, P., Wang, D., Miao, C.: Knowledge-enriched transformer for emotion detection in textual conversations. arXiv preprint arXiv:1909.10681 (2019)
Acknowledgments
We would like to thank three anonymous reviewers for their valuable comments. This work was supported by the Natural Science Foundation of China (No. 61672288). Xiao Jin and Jianfei Yu contributed equally to this paper.
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Jin, X., Yu, J., Ding, Z., Xia, R., Zhou, X., Tu, Y. (2020). Hierarchical Multimodal Transformer with Localness and Speaker Aware Attention for Emotion Recognition inĀ Conversations. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_4
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