Implementing Dialogue Emotion Recognition via Recursive Propagation of Explicit Emotions
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5669-8_15
Reference21 articles.
1. 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, pp. 13789–13797 (2021)
2. Shen, W., Wu, S., Yang, Y., Quan, X.: Directed Acyclic graph network for conversational emotion recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1551–1560 (2021)
3. Li, D., Wang, Y., Funakoshi, K., Okumura, M.: Joyful: Joint modality fusion and graph contrastive learning for multimoda emotion recognition. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 16051–16069 (2023)
4. Zhang, X., Li, Y.: A cross-modality context fusion and semantic refinement network for emotion recognition in conversation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13099–13110 (2023)
5. 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, pp. 6818–6825 (2019)
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