DAGN: Discourse-Aware Graph Network for Logical Reasoning
- URL: http://arxiv.org/abs/2103.14349v1
- Date: Fri, 26 Mar 2021 09:41:56 GMT
- Title: DAGN: Discourse-Aware Graph Network for Logical Reasoning
- Authors: Yinya Huang, Meng Fang, Yu Cao, Liwei Wang, Xiaodan Liang
- Abstract summary: 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.
- Score: 83.8041050565304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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