Utilization of Question Categories in Multi-Document Machine Reading Comprehension

Abstract

Multi-document machine reading comprehension has become a hot topic in natural language processing due to its more realistic setting and wider applications. However, how to effectively exploit the information of multiple documents and the question is still a challenge. In this paper, we propose a new end-to-end reading comprehension model with the utilization of question categories. To compress the search space of the answer and pinpoint it more precisely, we make the best use of the question and its category to predict the length of the answer. To better evaluate the importance of each document and give a more suitable score, we integrate the question category into multi-step reasoning based document extraction. Besides, we propose a new question classification model based on keyword extraction to get the question categories. The experimental results show that our method outperforms the baselines on the English MS MARCO dataset and the Chinese DuReader dataset.