Multi-hop Reading Comprehension Incorporating Sentence-Based Reasoning

Multi-hop machine reading comprehension (MRC) requires models to mine and utilize relevant information from multiple documents to predict the answer to a semantically related question. Existing work resorts to either document-level or entity-level inference among relevant information, which can be too coarse or too subtle, resulting less accurate understanding of the texts. To mitigate the issue, this research proposes a sentence-based multi-hop reasoning approach named SMR. SMR starts with sentences of documents, and unites the question to establish several reasoning chains based on sentence-level representations. In addition, to resolve the complication of pronouns on sentence semantics, we concatenate two sentences, if necessary, to assist in constructing reasoning chains. The model then synthesizes the information existed in all the reasoning chains, and predicts a probability distribution for selecting the correct answer. In experiments, we evaluate SMR on two popular multi-hop MRC benchmark datasets - WikiHop and MedHop. The model achieves 68.3 and 62.9 in terms of accuracy, respectively, exhibiting a remarkable improvement over state-of-the-art option. Additionally, qualitative analysis also demonstrates the validity and interpretability of SMR.