Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games

• URL: http://arxiv.org/abs/2004.02393v1
• Date: Mon, 6 Apr 2020 03:54:38 GMT
• Title: Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
• Authors: Yufei Feng, Mo Yu, Wenhan Xiong, Xiaoxiao Guo, Junjie Huang, Shiyu Chang, Murray Campbell, Michael Greenspan and Xiaodan Zhu
• Abstract summary: We propose a new problem of learning to recover reasoning chains from weakly supervised signals. How the evidence passages are selected and how the selected passages are connected are handled by two models. For evaluation, we created benchmarks based on two multi-hop QA datasets.
• Score: 66.98855910291292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and how the selected passages are connected are handled by two models that cooperate to select the most confident chains from a large set of candidates (from distant supervision). For evaluation, we created benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and hand-labeled reasoning chains for the latter. The experimental results demonstrate the effectiveness of our proposed approach.

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