Utilizing graph neural networks to improving dialogue based relation extraction
Abstract Relation extraction has been an active research interest in the field of Natural Language Processing (NLP) The past works primarily focused on a corpus of formal text which is inherently non dialogic. Recently, the dialogue based relation extraction task, which detects relations among speaker aware entities scattering in dialogues, has been gradually arousing people's attention. Some sequence based neural methods have been carried out to obtain the relevant information. However, identifying cross sentence relations remains unsolved, especially in the context of a specific domain dialogue system. In this paper, we propose a Relational Attention Enhanced Graph Convolutional Network (RAEGCN) which constructs the whole dialogue as a semantic interactive graph by emphasizing the speaker related information and leveraging various inter sentence dependencies. A dense connectivity mechanism is also introduced to empower the multi hop relational reasoning across sentences, which can capture both local and non local features simultaneously. Experiments show the significant superiority and robustness of our model on a real world dataset DialogRE, as compared with previous approaches.
Neurocomputing