Due to the lack of insufficient data, existing multi-hop open domain question
answering systems require to effectively find out relevant supporting facts
according to each question. To alleviate the challenges of semantic factual
sentences retrieval and multi-hop context expansion, we present a semantic
sentence composition reasoning approach for a multi-hop question answering
task, which consists of two key modules: a multi-stage semantic matching module
(MSSM) and a factual sentence composition module (FSC). With the combination of
factual sentences and multi-stage semantic retrieval, our approach can provide
more comprehensive contextual information for model training and reasoning.
Experimental results demonstrate our model is able to incorporate existing
pre-trained language models and outperform the existing SOTA method on the QASC
task with an improvement of about 9%.

针对多跳领域问题，通过多段语义匹配模块（MSSM）和事实句子构成模块（FSC）的结合，提出了一种语义句子组成推理方法，其能提供更全面的上下文信息，实验结果表明该模型能结合现有的预训练语言模型，在 QASC 任务上的表现优于现有的 SOTA 方法约 9%。