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    <abstract>We propose a novel Chain Guided Retriever-reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A Chain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.</abstract>
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%0 Conference Proceedings
%T Exploiting Reasoning Chains for Multi-hop Science Question Answering
%A Xu, Weiwen
%A Deng, Yang
%A Zhang, Huihui
%A Cai, Deng
%A Lam, Wai
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F xu-etal-2021-exploiting-reasoning
%X We propose a novel Chain Guided Retriever-reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A Chain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.
%R 10.18653/v1/2021.findings-emnlp.99
%U https://aclanthology.org/2021.findings-emnlp.99
%U https://doi.org/10.18653/v1/2021.findings-emnlp.99
%P 1143-1156

Markdown (Informal)

[Exploiting Reasoning Chains for Multi-hop Science Question Answering](https://aclanthology.org/2021.findings-emnlp.99) (Xu et al., Findings 2021)

• Exploiting Reasoning Chains for Multi-hop Science Question Answering (Xu et al., Findings 2021)

ACL

• Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, and Wai Lam. 2021. Exploiting Reasoning Chains for Multi-hop Science Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1143–1156, Punta Cana, Dominican Republic. Association for Computational Linguistics.