Dialogue Relation Extraction with Document-Level Heterogeneous Graph Attention Networks

Abstract

We propose a heterogeneous graph attention network to address the problem of dialogue relation extraction. Compared with several popular sequence-based and graph-based models, our method shows superior performance on the benchmark dataset DialogRE. The implementation of this work can be found at https://github.com/declare-lab/dialog-HGAT Dialogue relation extraction aims to detect the relation between pairs of entities mentioned in a multi-party dialogue. It plays an essential role in understanding the deep logic of dialogues and facilitating the development of intelligent dialogue systems. We introduce a heterogeneous graph attention network to model the cross-sentence relations in a conversation. This heterogeneous graph attention network has modeled multi-type features of the conversation, such as utterance, word, speaker, argument, and entity type information. We compare our method with several popular baselines such as convolutional neural networks and long short-term memory, experimental results show our model outperforms the state-of-the-art method by 9.4%/7.8% F1 scores, and 6.6%/3.9% $$F1_c$$ scores in both validation and test sets with only 4.0M parameters. In this work, we present an attention-based heterogeneous graph network to deal with the dialogue relation extraction task in an inductive manner. Experimental results on the dataset DialogRE confirm the effectiveness of our method.