Explainable Multi-Hop Question Answering: A Rationale-Based Approach

Multi-hop question answering tasks involve identifying relevant supporting sentences from a given set of documents, which serve as the rationale for deriving answers. Most research in this area consists of two main components: a rationale identification module and a reader module. Since the rationale identification module often relies on retrieval models or supervised learning, annotated rationales are typically essential. This reliance on annotations, however, creates challenges when adapting to open-domain settings. Moreover, when models are trained on annotated rationales, explainable artificial intelligence (XAI) requires clear explanations of how the model arrives at these rationales. Consequently, traditional multi-hop question answering (QA) approaches that depend on annotated rationales are ill-suited for XAI, which demands transparency in the model’s reasoning process. To address this issue, we propose a rationale reasoning framework that can effectively infer rationales and clearly demonstrate the model’s reasoning process, even in open-domain environments without annotations. The proposed model is applicable to various tasks without structural constraints, and experimental results demonstrate its significantly improved rationale reasoning capabilities in multi-hop question answering, relation extraction, and sentence classification tasks.