A Sentence-Based Circular Reasoning Model in Multi-Hop Reading Comprehension

Multi-hop Machine Reading Comprehension (MRC) requires models to mine and utilize relevant information from multiple documents to predict the answer to a semantics-related query. The existing researches resort to either document-level or entity-level inferences among relevant information, but such practices may be too coarse or too subtle to result less accurate understanding of the text. To address this issue, this research proposes a Sentence-based Circular Reasoning (SCR) approach, which starts with sentence representation and then unites the query to establish a reasoning path based on a loop inference unit. Further, the model synthesizes the information existing in the reasoning path and receives a probability distribution for selecting the correct answer. In addition, this study proposes a nested mechanism to extend the probability distribution for weighting. And it is proven that this mechanism can assist the model to perform better. Some experiments evaluate SCR on two popular multi-hop MRC benchmark datasets, WikiHop and MedHop, achieving 71.6 and 63.2 in terms of accuracy, respectively, and thus exhibiting competitive results compared with the state-of-the-art model. Additionally, qualitative analyses also demonstrate the validity and interpretability of SCR.