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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (4): 134-141    DOI: 10.11925/infotech.2096-3467.2020.0714
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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
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Abstract  

[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.

Key wordsMachine Reading Comprehension      Multi-Choice      Multi-Perspective Matching      Attention Mechanism     
Received: 21 July 2020      Published: 17 May 2021
ZTFLH:  分类号: TP391  
Fund:National Natural Science Foundation of China(61672040);National Natural Science Foundation of China(61972003)
Corresponding Authors: Duan Jianyong     E-mail: duanjy@ncut.edu.cn

Cite this article:

Duan Jianyong,Wei Xiaopeng,Wang Hao. A Multi-Perspective Co-Matching Model for Machine Reading Comprehension. Data Analysis and Knowledge Discovery, 2021, 5(4): 134-141.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0714     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I4/134

Overall Structure of the Model
Different Matching Strategies
数据集 RACE-M RACE-H RACE
子集 Train Dev Test Train Dev Test Train Dev Test all
文章数(篇) 6 409 368 362 18 728 1 021 1 045 25 137 1 389 1 407 27 933
问题数(个) 25 421 1 436 1 436 62 445 3 451 3 498 87 866 4 887 4 934 97 687
Information of the Dataset
模型 RACE-M RACE-H RACE
SAR 44.2 43.0 43.2
GA 43.7 44.2 44.1
ElimiNet - - 44.7
HAF 45.3 47.9 47.2
MUSIC 51.5 45.7 47.4
HCM 55.8 48.2 50.4
MRU 57.7 47.4 50.4
BERTbase
BERTbase+MPCM
71.1
72.5
62.3
63.1
65.0
66.1
Comparison of Experimental Results
问题类型 blank who when where what why which how title others
问题数量(个) 44 577 1 149 733 1 064 16 419 3 932 10 697 3 397 1 495 5 801
准确率(%) 67.3 55.4 56.1 64.3 60.9 67.2 60.4 63.5 66.7 60.5
The Number and Accuracy of Different Types of Questions
Accuracy of Questions with Different Length Answers
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