Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022)

Abstract: Multi-hop reasoning requires aggregating multiple documents to answer a complex question. Existing methods usually decompose the multi-hop question into simpler single-hop questions to solve the problem for illustrating the explainable reasoning process. However, they ignore grounding on the supporting facts of each reasoning step, which tends to generate inaccurate decompositions. In this paper, we propose an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-hop question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result. We employ a unified reader model for both intermediate hop reasoning and final hop inference and adopt joint optimization for more accurate and robust multi-hop reasoning. We conduct experiments on two benchmark datasets HotpotQA and 2WikiMultiHopQA. The results show that our method can effectively boost performance and also yields a better interpretable reasoning process without decomposition supervision.

## introduction recent years have witnessed an emerging trend in the task of multi-hop question answering. it requires the model to aggregate multiple pieces of documents (i.e., context) and perform multihop reasoning to infer the answer @xcite . several datasets have been introduced as benchmarks, such as hot-potqa @xcite , 2wikimultihopqa @xcite and wikihop @xcite , and the first two provide supporting facts supervision to encourage models to further explain what supporting sentences lead to the prediction. ## methodology given a multi-hop question q and a context including multiple paragraphs, we aim to read the question-relevant context c to predict the final answer a and explain it with the supporting sentences s. as illustrated in figure 2 , we present a stepwise reasoning framework to iteratively identify the single-hop supporting sentences and generate the single-hop question for the following reasoning, which consists of three components as below. it first filters out the unrelated paragraphs to extract the question-relevant context c ( § 2.1). then it identifies the supporting sentences of each intermediate hop from the relevant context to ask and answer the corresponding single-hop question, and passes the auto-generated messages to the next hop ( § 2.2). after intermediate hop reasoning ends, the last module predicts the final answer and the supporting sentences of the multi-hop question according to the preceding inference ( § 2.3). we jointly train a unified reader model for all reasoning hops ( § 2.4) and adopt two measures to mitigate the train-test discrepancy for better inference ( § 2.5). ## related work multi-hop question answering aims to aggregate multiple pieces of documents to model the multi-hop reasoning chain and predict the answer @xcite @xcite @xcite . prior methods mainly focus on utilizing a single reader to model the interaction between the question and relevant context, and simultaneously or separately predict the supporting sentences and answer within one step. @xcite and @xcite propose to construct graphs based on entity information from scattered paragraphs and utilize graph neural networks as the reader to reason out the answer. then hde-graph @xcite , hgn @xcite and sae @xcite enrich information in the entity graph by extending nodes of other granularity to build a hierarchical graph and improve the interaction between the question and context. some other methods @xcite @xcite adopt pre-trained models as the powerful reader for multi-hop reasoning and achieve promising results. however, these one-step reader methods directly encode the relevant context and question for answer prediction while neglecting to illustrate the explicit reasoning process. to circumvent the interpretability limitation, another stream of research proposes to solve the multihop reasoning by multi-step question decomposition. nishida et al. (2019) and jiang and bansal (2019 ) recurrently update the sub-query at each step to break down the problem but these subqueries are learned in latent representations and not sufficiently explainable. instead, some works explore to explicitly decompose the complex question into single-hop questions which assumes access to decomposition supervision @xcite . to skip this reliance, @xcite and @xcite attempt to construct pseudo-decomposition from other simple question corpora which pose a new challenge of label noises. besides, they only take as input the original question to generate single-hop questions without grounding on the supporting facts at each hop. in contrast, we design a stepwise reasoning framework to locate the single-hop supporting sentences at each step for generating more fact-grounded and informative single-hop sub-questions without any genuine or pseudo supervision, and integrate the sequential reasoning process into a unified multi-hop reader for more robust performance. ## conclusion in this paper, we study the task of multi-hop question answering and propose to stepwise locate the single-hop supporting sentences and generate more fact-grounded single-hop questions for better interpretable multi-hop reasoning. we present a stepwise reasoning framework to incorporate both single-hop supporting sentence identification and the corresponding single-hop question generation for each intermediate step until inferring a final result. it employs a pre-trained simple question generator and takes the identified single-hop supporting sentences as base to generate the single-hop question, which obviates the necessity of constructed supervision and helps generate more fact-based single-hop questions. it utilizes a unified reader to jointly learn both intermediate hop reasoning and final hop inference for better fault tolerance. experimental results validate the general effectiveness and interpretability of our stepreasoner.