We propose a novel Chain Guided Retriever-reader ({\tt 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 \textit{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.

提出一种无需语料库特定注释的 Chain Guided Retriever-reader（CGR）框架，通过生成由语义图构建的推理链，提高多跳科学问答的可解释性和性能，采用增强学习训练检索器，并设计了适用于本地和全局链信息的 Chain-aware loss 作为培训的远程监督信号。