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Paper Abstract and Keywords
Presentation 2016-12-22 09:30
AI-based Japanese Short-answer Scoring and Support System
Tsunenori Ishioka, Masayuki Kameda (NCUEE), Dongyue Louis Liu (GRI) NLC2016-36
Abstract (in Japanese) (See Japanese page) 
(in English) We have developed an automated Japanese short-answer scoring and support machine for new National Center Test written exams. Our approach is based on the fact that recognizing textual entailment and/or synonymy has been almost impossible for scoring systems for several years. The system generates automated scores on the basis of evaluation criteria or rubrics, and human raters revise the scores. The system determines semantic similarity between the model answers and the actual written answers as well as a certain degree of semantic identity and implication. Owing to the need for the scoring results to be classified at multiple levels, we use random forests to effectively utilize many predictors rather than use support vector machines. An experimental prototype operates as a web system on a Linux computer. We compared human scores with the automated scores for the case in which 3-6 allotment points were placed in 8 categories of the social studies test as a trial examination. The differences between the scores were within one point for 70-90 percent of the data when high semantic judgment was not needed.
Keyword (in Japanese) (See Japanese page) 
(in English) Writing test / Automated scoring / Machine learning / Random forests / Recognizing textual entailment / Ministry of education / university and high-school articulation /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 379, NLC2016-36, pp. 87-92, Dec. 2016.
Paper # NLC2016-36 
Date of Issue 2016-12-14 (NLC) 
ISSN Print edition: ISSN 0913-5685  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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF NLC2016-36

Conference Information
Committee SP IPSJ-SLP NLC IPSJ-NL  
Conference Date 2016-12-20 - 2016-12-22 
Place (in Japanese) (See Japanese page) 
Place (in English) NTT Musashino R&D 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 18th Spoken Language Symposium & The Third Natural Language Processing Symposium 
Paper Information
Registration To NLC 
Conference Code 2016-12-SP-SLP-NLC-NL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) AI-based Japanese Short-answer Scoring and Support System 
Sub Title (in English)  
Keyword(1) Writing test  
Keyword(2) Automated scoring  
Keyword(3) Machine learning  
Keyword(4) Random forests  
Keyword(5) Recognizing textual entailment  
Keyword(6) Ministry of education  
Keyword(7) university and high-school articulation  
Keyword(8)  
1st Author's Name Tsunenori Ishioka  
1st Author's Affiliation The Nat. Ctr. for Univ. Entrance Exam. (NCUEE)
2nd Author's Name Masayuki Kameda  
2nd Author's Affiliation The Nat. Ctr. for Univ. Entrance Exam. (NCUEE)
3rd Author's Name Dongyue Louis Liu  
3rd Author's Affiliation Gakken Research Institute for Learning and Education (GRI)
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Speaker
Date Time 2016-12-22 09:30:00 
Presentation Time 30 
Registration for NLC 
Paper # IEICE-NLC2016-36 
Volume (vol) IEICE-116 
Number (no) no.379 
Page pp.87-92 
#Pages IEICE-6 
Date of Issue IEICE-NLC-2016-12-14 


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