Abstract: The task of emotion-cause pair extraction is to extract emotion clauses and reason clauses simultaneously.Previous methods regard emotion-cause pair extraction as three independent tasks of emotion extraction,cause extraction,and emotion-cause pair extraction,which cannot effectively capture the connection between tasks.In addition,the existing two-stage models suffer from error propagation problems,and the relative position distribution between emotion clauses and reason clauses is unbalanced.This paper proposes a new emotional reason pair extraction model MK-BERT based on BERT,sentiment lexicon and position-aware interaction module.The model first uses the BERT enhanced by the sentiment lexicon for document encoding.In order to solve the problem of label position imbalance,a position-aware interaction module is designed according to the relative distance between the emotion clause and the reason clause to capture the position information and construct the characteristics of the emotion-cause pair.Then,through interactive encoding between the emotion prediction module and the reason prediction mo-dule,the shared information among multiple tasks is fully mined.Experimental results on the Chinese emotion-reason pair extraction dataset show that the proposed modelcan effectively extract emotion-reason pairs and achieve good performance on positio-nally imbalanced samples.

Key words: Sentiment analysis, Emotion-Cause pair extraction, Multi-task learning, Sentiment lexicon, Position aware