A Three-step Method for Multi-Hop Inference Explanation Regeneration (2021)

Abstract: Multi-hop inference for explanation generation is to combine two or more facts to make an inference. The task focuses on generating explanations for elementary science questions. In the task, the relevance between the explanations and the QA pairs is of vital importance. To address the task, a three-step framework is proposed. Firstly, vector distance between two texts is utilized to recall the top-K relevant explanations for each question, reducing the calculation consumption. Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts. Thirdly, we adopt a re-ranking module to re-rank the retrieved candidate explanations with relevance between each fact and the QA pairs. Experimental results illustrate the effectiveness of the proposed framework with an improvement of 39.78% in NDCG over the official baseline.

## introduction multi-hop inference for explanation generation @xcite , aiming to combing two or more facts to make an inference and providing users with human-readable explanations, has shown significant potential and alluring technological value to improve medical or judicial systems. a typical application in natural language processing is question answering tasks (qa). multi-hop explanation generation for qa aims to retrieve multiple textual facts from pre-defined candidates (typically retrieved from different books, web pages, or other documents) for a given question-answer pair. figure 1 shows an example. the input is a qa sample and candidate facts, and the task is designed to retrieve facts f 1 , f 2 , f 3 , which contribute greatly to inferring the answer. multi-hop explanation generation for qa suffers from a key issue: computationally prohibitory, which causes by unaffordable amount of fact combinations, especially when the number of facts required to perform an inference increases. empirically speaking, the issue causes large drops in performance @xcite and limits the inference capacity @xcite . to solve the issue, previous works compute scores for facts in isolation, or by severely limiting the number of combinations of facts @xcite @xcite . @xcite proposed a two-step inference algorithm for multi-hop explanation regeneration with a relevant fact recall step and an autoregressive fact selection step. in this way, the two-step algorithm prompts efficiency and accuracy. in the textgraphs 2021 shared task, the relevance between the explanations and the qa pairs is of vital importance. however, the autogression selection process may hinder model's ability to recognize the relevance between each fact and qa. the main reason is that the autogression selection proceess emphasizes the relevance between qa and the retrieved facts, paying more attention on retrieved facts when there are many retrieved facts. as the example in figure 2 , the two-step algorithm fails to recognize the order of the retrieved two facts form means kind and ultraviolet rays means ultraviolet light. to ad-dress the problem, we propose a reranking module to fine-rank the results of the two-step method with the relevance between each fact and the qa pair. then, we propose a three-step framework to solve the task: recall, selection and reranking, aiming to iteratively recall facts, select core facts, and then rerank retrieved core facts, respectively. experiments on the 2021 version of the task demonstrate the effectiveness of the proposed method, which achieves improvements of 39.78% in ndcg, in comparison with the official baseline. ## method the proposed framework is designed to predict a ranked list of facts inferring a qa sample, including three modules: a recall, selection and reranking module, as illustrated in figure 2 . ## conclusion we proposed our approach to the shared task on "multi-hop inference explanation regeneration". our framework consists of three modules: a recall module, a selection module and a reranking module. the recall module retrieves top-k relevant facts using the distances between tf-idf vectors. then an antoregressive fact selection module is applied to predict the next fact considering the retrived facts. finally a rerank module is applied to correct the order. the proposed framework achieved an improvement of 39.78% over the official baseline.