%0 Conference Proceedings %T Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps %A Ho, Xanh %A Duong Nguyen, Anh-Khoa %A Sugawara, Saku %A Aizawa, Akiko %Y Scott, Donia %Y Bel, Nuria %Y Zong, Chengqing %S Proceedings of the 28th International Conference on Computational Linguistics %D 2020 %8 December %I International Committee on Computational Linguistics %C Barcelona, Spain (Online) %F ho-etal-2020-constructing %X A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do not require multi-hop reasoning to answer a question. In this study, we present a new multi-hop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data. In our dataset, we introduce the evidence information containing a reasoning path for multi-hop questions. The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model. We carefully design a pipeline and a set of templates when generating a question-answer pair that guarantees the multi-hop steps and the quality of the questions. We also exploit the structured format in Wikidata and use logical rules to create questions that are natural but still require multi-hop reasoning. Through experiments, we demonstrate that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required. %R 10.18653/v1/2020.coling-main.580 %U https://aclanthology.org/2020.coling-main.580 %U https://doi.org/10.18653/v1/2020.coling-main.580 %P 6609-6625