Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting among tokens. In this work, we explore aggregating passage-level clues for solving logical reasoning QA by using discourse-based information. We propose a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts. The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks. Experiments are conducted on two logical reasoning QA datasets, ReClor and LogiQA, and our proposed DAGN achieves competitive results. The source code is available at https://github.com/Eleanor-H/DAGN.

翻译：在这项工作中,我们利用基于对话的信息,探索如何汇集通过层次的线索,解决逻辑推理QA;我们建议建立一个以文本的谈话结构为根据的讲解图像网络(DAGN);模型将讨论信息编成图表,用基本讲解单元(EDUs)和讲解关系作为图表,并通过下游讲解任务图网络学习讲解-觉特征;对QA数据集(ReClor和LogiQA)进行了两个逻辑推理实验,我们提议的DAGN取得了竞争性结果。源代码见https://github.com/Eleanor-H/DAGN。