融合依存关系的对话关系抽取

Dialogue relationship extraction with dependency relation

为了提高对话中实体对的关系抽取能力,将依存关系引入到异构图注意力网络中,提出了DEP-GAT模型。首先,通过预处理层获取每个词的基本特征,然后在话语编码层实现上下文特征的抽取,并加入依存信息进一步掌握话语结构。最后利用特征构建异构图,设计有效的消息传递机制,从而使得更新后的对话实体对包含了整个对话的上下文信息和语法特征,以此提高模型对实体关系抽取的能力。实验结果表明,在DialogRE数据集上,DEP-GAT模型相比于基线模型,F 1值在开发集上提高了2.9%,在测试集上提高了1.8%。

In order to enhance the ability to extract entity pair relationship in dialogues,this paper proposes a DEP-GAT model by introducing dependency relation into a heterogeneous graph attention network.Initially,the basic characteristics of each word are obtained through the preprocessing layer.Subsequently,in the discourse coding layer,context features are extracted and dependency information is added to further understand the speech structure.Eventually,a heterogeneous graph is constructed by utilizing the features,and an effective message passing mechanism is designed to enable the updated dialogue entity pairs to contain all the context information and grammatical features of the entire dialogue,thereby further enhancing the ability of the model to extract entity relations.The experimental results reveal that,on the DialogRE data set,the DEP-GAT model performs better than the baseline model does,with an increased F1 value of 2.9%in the development set and 1.8%in the test set respectively.