An Interpretable model using evidence information for Multi-hop Question Answering over Long texts

Machine Reading Comprehension (MRC) is a challenging task in natural language understanding, especially multi-hop question answering (QA) in long texts. One of the challenges in multi-hop QA requires models to produce interpretable answers based on evidence that is selected from a given long text. Based on the Retriever-Reader architecture, existing work tackles this problem by using different methods to exploit various evidence information. To better use evidence information, we propose a loss function considering answer groups, which improves the reasoning ability of the reader in the Retriever-Reader architecture. Besides, we introduce the relevance constraint factor containing evidence information to improve the reader's ability of locating key sentences. Evaluated on the HotpotQA dataset, the proposed methods achieve improvement, demonstrating the effectiveness of our methods and the importance of evidence information.