Abstract
Emotion recognition in conversations (ERC) aims to predict the emotion of utterances. Modeling context dependencies is the critical challenge of the task. Existing efforts in ERC are mainly based on the sequence and graph models. The graph models can better capture structured information than the sequence models. Unfortunately, there are few suitable aggregation strategies for ERC models based on high-dimensional edge features. Moreover, the adjustment of edge representation in graph-based models has been ignored for a long time. Based on this, we propose a learnable edge message-passing model based on a heterogeneous dialog graph. The model first calculates the attention weights between utterance nodes and between nodes and edges separately and then learns contextual utterance representations through these learnable edge representations. Additionally, we conducted our experiment on four public datasets and achieved advanced results.
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References
Adikari, A., De Silva, D., Alahakoon, D., Yu, X.: A cognitive model for emotion awareness in industrial chatbots. In: Proceedings of INDIN, vol. 1, pp. 183–186 (2019)
Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., Choi, Y.: COMET: commonsense transformers for automatic knowledge graph construction. In: Proceedings of ACL, pp. 4762–4779 (2019)
Busso, C., et al.: IEMOCAP: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42(4), 335–359 (2008)
Ghosal, D., Majumder, N., Gelbukh, A., Mihalcea, R., Poria, S.: Cosmic: commonsense knowledge for emotion identification in conversations. In: Findings of ACL: EMNLP 2020, pp. 2470–2481 (2020)
Ghosal, D., Majumder, N., Poria, S., Chhaya, N., Gelbukh, A.: DialogueGCN: a graph convolutional neural network for emotion recognition in conversation. In: Proceedings of EMNLP-IJCNLP, pp. 154–164 (2019)
Hazarika, D., Poria, S., Mihalcea, R., Cambria, E., Zimmermann, R.: Icon: interactive conversational memory network for multimodal emotion detection. In: Proceedings of EMNLP, pp. 2594–2604 (2018)
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 EMNLP, pp. 7360–7370 (2020)
Jiang, X., Ji, P., Li, S.: CensNet: convolution with edge-node switching in graph neural networks. In: Proceedings of IJCAI, pp. 2656–2662 (2019)
Li, J., Lin, Z., Fu, P., Wang, W.: Past, present, and future: conversational emotion recognition through structural modeling of psychological knowledge. In: Findings of ACL: EMNLP 2021, pp. 1204–1214 (2021)
Li, J., Ji, D., Li, F., Zhang, M., Liu, Y.: HiTrans: a transformer-based context-and speaker-sensitive model for emotion detection in conversations. In: Proceedings of COLING, pp. 4190–4200 (2020)
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S.: DailyDialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of IJCNLP, pp. 986–995 (2017)
Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: DialogueRNN: an attentive RNN for emotion detection in conversations. In: Proceedings of AAAI, 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 ACL, 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. In: Proceedings of ACL, pp. 527–536 (2019)
Poria, S., Majumder, N., Mihalcea, R., Hovy, E.: Emotion recognition in conversation: research challenges, datasets, and recent advances. IEEE Access 7, 100943–100953 (2019)
Shen, T., et al.: PEIA: personality and emotion integrated attentive model for music recommendation on social media platforms. In: Proceedings of AAAI, vol. 34, pp. 206–213 (2020)
Shen, W., Chen, J., Quan, X., Xie, Z.: DialogXL: All-in-one XLNet for multi-party conversation emotion recognition. In: Proceedings of AAAI, vol. 35, pp. 13789–13797 (2021)
Shen, W., Wu, S., Yang, Y., Quan, X.: Directed acyclic graph network for conversational emotion recognition. In: Proceedings of ACL-IJCNLP, pp. 1551–1560 (2021)
Shi, Y., Huang, Z., Wang, W., Zhong, H., Feng, S., Sun, Y.: Masked label prediction: unified massage passing model for semi-supervised classification. In: Proceedings of IJCAI (2021)
Vaswani, A., et al.: Attention is all you need. In: NIPS 30 (2017)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. Stat 1050, 20 (2017)
Wang, Z., Chen, J., Chen, H.: EGAT: edge-featured graph attention network. In: Proceedings of ICANN, pp. 253–264 (2021)
Yang, Y., Li, D.: NENN: Incorporate node and edge features in graph neural networks. In: Proceedings of ACML, pp. 593–608 (2020)
Zahiri, S.M., Choi, J.D.: Emotion detection on tv show transcripts with sequence-based convolutional neural networks. In: Workshops of AAAI (2018)
Acknowledgments
This work was partially supported by the National Nat- ural Science Foundation of China (61876053, 62006062, 62176076), Shenzhen Foundational Research Funding (JCYJ20200109113441941 and JCYJ2021032411 5614039), Joint Lab of HIT and KONKA.
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Li, Z., Tu, G., Liang, X., Xu, R. (2022). Developing Relationships: A Heterogeneous Graph Network with Learnable Edge Representation for Emotion Identification in Conversations. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_25
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