AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension

• URL: http://arxiv.org/abs/2203.08992v1
• Date: Wed, 16 Mar 2022 23:51:01 GMT
• Title: AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
• Authors: Xiao Li, Gong Cheng, Ziheng Chen, Yawei Sun, Yuzhong Qu
• Abstract summary: Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. We present a neural-symbolic approach which, to predict an answer, passes messages over a graph representing logical relations between text units.
• Score: 21.741085513119785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot directly apply to text. To meet the challenge, we present a neural-symbolic approach which, to predict an answer, passes messages over a graph representing logical relations between text units. It incorporates an adaptive logic graph network (AdaLoGN) which adaptively infers logical relations to extend the graph and, essentially, realizes mutual and iterative reinforcement between neural and symbolic reasoning. We also implement a novel subgraph-to-node message passing mechanism to enhance context-option interaction for answering multiple-choice questions. Our approach shows promising results on ReClor and LogiQA.

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