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Paper Abstract and Keywords
Presentation 2021-12-16 14:55
Fully automatic scoring of handwritten descriptive answers in Japanese language tests
Hung Tuan Nguyen, Cuong Tuan Nguyen (TUAT), Haruki Oka (UTokyo), Tsunenori Ishioka (The National Center for University Entrance Examinations), Masaki Nakagawa (TUAT) PRMU2021-32
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents an experiment of automatically scoring handwritten descriptive answers in the trial tests for the new Japanese university entrance examination, which were made for about 120,000 examinees in 2017 and 2018. There are about 400,000 answers with more than 20 million characters. Although all answers have been scored by human examiners, handwritten characters are not labelled. We present our attempt to adapt deep neural network-based handwriting recognizers trained on a labelled handwriting dataset into this unlabeled answer set. Our proposed method combines different training strategies, ensembles multiple recognizers, and uses a language model built from a large general corpus to avoid overfitting into specific data. In our experiment, the proposed method records character accuracy of over 97% using about 2,000 verified labelled answers that account for less than 0.5% of the dataset. Then, the recognized answers are fed into a pre-trained automatic scoring system based on the BERT model without correcting misrecognized characters and providing rubric annotations. The automatic scoring system achieves from 0.84 to 0.98 of Quadratic Weighted Kappa (QWK). As QWK is over 0.8, it represents acceptable similarity of scoring between the automatic scoring system and the human examiners. These results are promising for further research on end-to-end automatic scoring of descriptive answers.
Keyword (in Japanese) (See Japanese page) 
(in English) handwritten language answers / handwriting recognition / automatic scoring / ensemble recognition / deep neural networks / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-32, pp. 45-50, Dec. 2021.
Paper # PRMU2021-32 
Date of Issue 2021-12-09 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee PRMU  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2021-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fully automatic scoring of handwritten descriptive answers in Japanese language tests 
Sub Title (in English)  
Keyword(1) handwritten language answers  
Keyword(2) handwriting recognition  
Keyword(3) automatic scoring  
Keyword(4) ensemble recognition  
Keyword(5) deep neural networks  
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1st Author's Name Hung Tuan Nguyen  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Cuong Tuan Nguyen  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
3rd Author's Name Haruki Oka  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Tsunenori Ishioka  
4th Author's Affiliation The National Center for University Entrance Examinations (The National Center for University Entrance Examinations)
5th Author's Name Masaki Nakagawa  
5th Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2021-12-16 14:55:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-32 
Volume (vol) vol.121 
Number (no) no.304 
Page pp.45-50 
#Pages 6 
Date of Issue 2021-12-09 (PRMU) 

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