基于位置交互感知网络的多任务情绪原因对抽取方法

Multi-task Emotion-Cause Pair Extraction Method Based on Position-aware Interaction Network

情绪原因对抽取任务旨在同时抽取情感子句和原因子句。已有的方法把情绪原因对抽取看作情绪抽取、原因抽取和情绪原因对抽取3个独立的任务,不能有效捕捉到任务之间的联系。此外,现有的两阶段模型存在误差传播问题,并且情绪子句和原因子句间相对位置分布不平衡。文中提出了一个新的基于BERT、情感词典和位置感知交互模块的情绪原因对抽取模型MK-BERT。该模型首先用情感词典增强的BERT进行文本编码;其次,为了解决标签位置不平衡问题,根据情感子句和原因子句间的相对距离设计位置感知交互模块,以捕捉位置信息并构建情绪原因对的特征;最后,通过情绪预测模块和原因预测模块间交互编码,充分挖掘多个任务间的共享信息。在中文情绪原因对抽取数据集上进行实验,结果表明,所提模型可以有效地抽取情绪原因对,并且在位置不平衡样本上取得良好性能。

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 module,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 positionally imbalanced samples.