Sen-BiGAT-Inter:情绪原因对抽取方法

情绪原因对抽取任务是将情绪子句与原因子句同时抽取。针对该任务,现有模型的编码层未考虑强化情感词语义表示,且仅使用单一图注意力网络,因此,该文提出了一个使用情感词典、图网络和多头注意力的情绪原因对抽取方法(Sen-BiGAT-Inter)。该方法首先利用情感词典与子句中的情感词汇匹配,并将匹配的情感词汇与该子句进行合并,再使用预训练模型BERT(Bidirectional Encoder Representation from Transformers)对句子进行表示。其次,建立两个图注意力网络,分别学习情绪子句和原因子句表示,进而获取候选情绪原因对的表示。在此基础上,应用多头注意力交互机制学习候选情绪原因对的全局信息,同时结合相对位置信息得到候选情绪原因对的表示,用于实现情绪原因对的抽取。在中文情绪原因对抽取数据集上的实验结果显示,相比目前最优的结果,该文所提出的模型在 F₁ 值上提升约1.95。

Sen-BiGAT-Inter: A Method for Emotion-Cause Pair Extraction

Emotion-cause pair extraction is to extract both emotion clause and cause clause at the same time. For this task, the existing method of a single graph attention network does not consider emphasize the semantic representation of emotion words in the encoding layer. This paper proposes a Sen-BiGAT-Inter method using sentiment lexicon, graph network and multi-attention. The proposed method uses the sentiment lexicon to merge this clause with the emotion words in the clause, and uses the pre-training model BERT (Bidirectional Encoder Representation from Transformers) to obtain the clause representation. Then, we build two graph attention networks to learn the representation of emotion clause and cause clause, respectively, and then obtain the representation of candidate emotion-cause pair. On this basis, we get the emotion-cause pair with causality by using multi-head attention to learn the global information of candidate sentence pairs, and combing the relative position information to get the final representation of pairs. The experimental results on Chinese emotion-cause pair extraction dataset show the proposed model improves the F₁ value by about 1.95 compared with the current optimal results.