Applied Sciences, Vol. 13, Pages 12156: DaGATN: A Type of Machine Reading Comprehension Based on Discourse-Apperceptive Graph Attention Networks

Applied Sciences, Vol. 13, Pages 12156: DaGATN: A Type of Machine Reading Comprehension Based on Discourse-Apperceptive Graph Attention Networks

Applied Sciences doi: 10.3390/app132212156

Authors: Mingli Wu Tianyu Sun Zhuangzhuang Wang Jianyong Duan

In recent years, with the advancement of natural language processing techniques and the release of models like ChatGPT, how language models understand questions has become a hot topic. In handling complex logical reasoning with pre-trained models, its performance still has room for improvement. Inspired by DAGN, we propose an improved DaGATN (Discourse-apperceptive Graph Attention Networks) model. By constructing a discourse information graph to learn logical clues in the text, we decompose the context, question, and answer into elementary discourse units (EDUs) and connect them with discourse relations to construct a relation graph. The text features are learned through a discourse graph attention network and applied to downstream multiple-choice tasks. Our method was evaluated on the ReClor dataset and achieved an accuracy of 74.3%, surpassing the best-known performance methods utilizing deberta-xlarge-level pre-trained models, and also performed better than ChatGPT (Zero-Shot).