Abstract
Emotion detection in conversations has become a very important and challenging task. Most of previous studies do not distinguish different speakers in a dialogue and fail to characterize inter-speaker dependencies. In this paper, we propose Speaker Influence-aware Neural Network model (SINN) to predict the emotion of the last utterance in a conversation, which explicitly models the self and inter-speaker influences of historical utterances with GRUs and hierarchical attention matching network. Moreover, the empathy phenomenon is also considered by an emotion state tracking component in SINN. Finally, the target utterance representation is enhanced by speaker influence aware context modeling, where the attention mechanism is used to extract the most relevant features for emotion classification. Experiment results on DailyDialog dataset confirm that our model consistently outperforms the state-of-the-art methods.
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Acknowledgements
The work was supported by the National Key R&D Program of China under grant 2018YFB1004700, and National Natural Science Foundation of China (61872074, 61772122).
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Wei, J., Feng, S., Wang, D., Zhang, Y., Li, X. (2019). Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_25
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DOI: https://doi.org/10.1007/978-3-030-32236-6_25
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