FW-ECPE: An Emotion-Cause Pair Extraction Model Based on Fusion Word Vectors

Emotion-Cause Pair Extraction (ECPE) aims to extract potential emotion-cause pairs from text without emotion labels. It lays an important foundation for downstream research such as causal reasoning, public opinion prediction, and reason detection. However, the ECPE task now faces two dilemmas: 1) insufficient utilization of word sequence information, and 2) inadequate use of position information between clauses. To address the above problems, we proposed an Emotion-Cause Pair Extraction model based on Fusion Word Vectors named FW-ECPE. It is a two-stage model that first extracts emotion clauses and cause clauses respectively then combines them into pairs and filters out the right emotion-cause pairs. The Fusion Word Vector is reflected in two aspects. Firstly, we integrate the clause context vectors and the emotion clauses prediction results with cause context vectors in cause clauses extraction. Secondly, in the emotion-cause pair extraction stage, we fuse the position information between clauses and contextual information. Finally, we extend Easy Data Augmentation, a corpus enhancement algorithm, to enlarge the amount of data and alleviate the risk of overfitting. The experiment results show that our proposed approach outperforms the previous methods on a benchmark dataset.