Ask to Understand: Question Generation for Multi-hop Question Answering (2023)

Abstract: “Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thusachieving good performance. At the same time, the latter provides a clear reasoning logical routeby decomposing multi-hop questions into simple single-hop sub-questions. In this paper, wepropose a novel method to complete multi-hop QA from the perspective of Question Genera-tion (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classicalQA module, which could help the model understand the context by asking inherently logicalsub-questions, thus inheriting interpretability from the QD-based method and showing superiorperformance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of ourproposed QG module, human evaluation further clarifies its interpretability quantitatively, andthorough analysis shows that the QG module could generate better sub-questions than QD meth-ods in terms of fluency, consistency, and diversity.”

## conclusion in this paper, drawing inspiration from human cognitive behavior, we posit that the act of asking questions serves as a crucial indicator for determining whether a model genuinely comprehends the input text. consequently, we introduce a qg module designed to tackle multi-hop qa tasks in an interpretable manner. building upon traditional qa modules, the incorporation of the qg module effectively enhances natural language understanding capabilities, delivering superior and robust performance through the process of asking questions. furthermore, we conduct a quantitative analysis of interpretability, as provided by sub-questions, utilizing human evaluation and elucidating interpretability through attention visualization. ultimately, we substantiate that the sub-questions derived from the qg method surpass those obtained via the qd method in terms of linguistic uency, consistency, and diversity, underscoring the bene ts of our proposed approach.