Abstract
The goal of Emotion-Cause Pair Extraction is to extract the emotion clause and the corresponding cause clause from the unmarked document level text. The two steps solution has the disadvantage of error propagation and fails to make full use of context information. In this paper, we propose an end to end neural network. The model uses Transformer with different granularity to extract hierarchical text features, and uses the text capsule network to model the relationship between the local text features of emotion cause pair and the overall causal relationship. We also utilize a filter mechanism to alleviate the problem of sample imbalance of ECPE task. The experimental results show that our model has achieved great performance improvement, which is higher than most baseline methods on F1 score.
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Yang, C., Ding, J. (2022). Emotion-Cause Pair Extraction via Transformer-Based Interaction Model with Text Capsule Network. 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_60
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