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

Published onJun 08, 2021
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
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Abstract

We extend the formats of explanations in interpretable NLP with the proposed entity-centric reasoning chains for multi-hop question answering. We also propose a cooperative game approach to learn to recover such explanations from weakly supervised signals, i.e., the question-answer pairs. We evaluate our task and method via newly created benchmarks based on two multi-hop datasets, HotpotQA and MedHop; and hand-labeled reasoning chains for the latter. The experiments demonstrate the effectiveness of our approach.

Article ID: 2021S12

Month: May

Year: 2021

Address: Online

Venue: Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association

URL: https://caiac.pubpub.org/pub/ncwsy975/

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