A Multi-Perspective Co-Matching Model for Machine Reading Comprehension
Duan Jianyong1,2(),Wei Xiaopeng1,Wang Hao1,2
1School of Information, North China University of Technology, Beijing 100144, China 2CNONIX National Standard Application and Promotion Laboratory, North China University of Technology, Beijing 100144, China
[Objective] This paper proposes a model for multiple-choice reading comprehension, and then explores the impacts of question types and answer length on machine reading comprehension. [Methods] First, we used the multi-perspective matching mechanism to obtain the correlation between the articles, questions and candidate answers. Then, we multiplied the correlation and articles to create the vector representation of questions and candidate answers. Third, we extracted sentence-level and document-level features, which were used to select the correct answers. Fourth, we categorized the data based on the question types and answer length. Finally, we analyzed their impacts on the machine’s choice of correct answers. [Results] The accuracy of our model on the RACE-M, RACE-H and RACE datasets reached 72.5%, 63.1% and 66.1% respectively. [Limitations] The multi-perspective matching mechanism has four matching strategies and multiple angles, which makes the model consume a lot of memory and spend longer processing time at the interactive layer. [Conclusions] The proposed model can effectively match articles with questions and answers. The accuracy of the model is more affected by the type of question, not by the length of answer.
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