Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2021

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 ARCChallenge, but also favors explainability

Keywords

Ground truth, Multi-hops, Question Answering, Reasoning process, Semantic graphs

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the Findings of the Association for Computational Linguistics, EMNLP 2021, Punta Cana, Dominican Republic, November 7-11

First Page

1143

Last Page

1156

ISBN

9781955917100

Identifier

10.18653/v1/2021.findings-emnlp.99

Publisher

Association for Computational Linguistics

City or Country

Texas

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.18653/v1/2021.findings-emnlp.99

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