Emotion-Cause Relationship Between Clauses Prediction: a Novel Method Based on BERT for Emotion-Cause Pair Extraction

Rui Fan, Tingting He, Yufan Wang, Jie Hong, Xinhui Tu

Abstract: Emotion-cause pair extraction (ECPE) aims to obtain all emotion-cause pairs consisting of the emotion clause and the corresponding cause clause in a document. Many existing works for ECPE utilize BERT to obtain representation on each clause in the document, and then perform the classification of Cartesian product among all clause representations or the clause-level sequence tagging. In this paper, we propose to redefine ECPE as the emotion-cause relationship between clauses prediction (ECRP). ECRP fits well with the form of the next sentence prediction task in BERT, which effectively unifies the BERT’s pre-training and the ECPE-specific fi ne-tuning process. According to the task form of ECRP, we reconstruct the original ECPE dataset from the document format to the clause-pair format. The scale of data is effectively expanded, and the imbalance of data is alleviated to a certain extent because some redundant data is filtered out based o n t he r elative distance between clauses. Experiments demonstrate that our ECRP-BERT model outperforms many competitive baselines. Especially in the case of low resources, the ECRP-BERT model still achieves a good performance.