基于图依存分析的情感原因对抽取任务

情感原因对抽取是情感分析任务中的子任务，旨在抽取出给定文档中的所有情感子句以及引起该情感所对应的原因子句。先前的研究在生成情感子句与原因子句表示时忽略了情感子句与原因子句之间的相互联系。为了解决上述问题，基于图依存分析的思想并融入了图注意力机制，提出了GAT-ECPE模型。该模型在获取到情感子句表示与原因子句表示时，将句向量作为节点输入图注意力层从而学习到子句之间关系的信息，而后进行双仿射映射得到情感原因对的编码表示。并且设置了多任务来将情感抽取与原因抽取任务建立联系。在ECPE数据集上的实验结果证明，本文模型相较于先前的一系列模型，在评估指标上有所提升。

Task of emotion cause pair extraction based on graph-based dependency parsing

Emotion cause pair extraction is a subtask in the sentiment analysis task, which aims to extract all emotion clauses in a given document and the cause clauses corresponding to the emotion. The previous work ignores the interre-lationship between the emotion clause and the cause clause when generating the expression of the emotion clause and the cause clause. In order to solve the problem, based on the idea of graph-based dependency parsing and incorporating the graph attention mechanism, this paper proposed the GAT-ECPE model. When the model obtained the expression of the emotion clause and the reason clause, it used the sentence vector as a node into the graph attention layer to learn the information about the relationship between the clauses, and then performed biaffifine transform to obtain the encoding of the emotion cause pair expression. And it set up a multi-task to establish a relationship between the extraction of emotions and causes. The experimental results on the ECPE data set prove that compared with the previous series of models, this model has improved evaluation indicators.