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Paper Abstract and Keywords
Presentation 2026-03-25 09:50
Contrastive Learning Network for Zero-Shot Automatic Scoring of Very Short Answers
Tuan Nam Ly, Hung Tuan Nguyen, Thanh-Nghia Truong, Masaki Nakagawa (TUAT) PRMU2025-58
Abstract (in Japanese) (See Japanese page) 
(in English) Most automatic scoring methods for handwritten answers are based on handwriting recognizers. However, handwriting recognizers typically rely on predefined dictionaries, which can lead to false positives when encountering out-of-vocabulary characters or characters that resemble dictionary entries. This is particularly challenging for beginners learning languages. To address this limitation, this paper proposes a Contrastive Learning Network-based automatic scoring method to improve the scoring of single- or few-character answers. The proposed method comprises two main components: a Contrastive Learning Network-based Pattern Similarity and a Pattern Similarity-based Automatic Scoring Algorithm. The proposed Contrastive Learning Network is based on the Siamese network architecture, which comprises two identical backbones to calculate the similarity between the two input images. It is trained using a contrastive loss function on pairs of two input answers and their similarity. The pattern-similarity-based automatic scoring algorithm scores an answer as correct, incorrect, or rejected according to its similarity to the expected answer. To train the Contrastive Learning Network, we propose a novel data-sampling method using a dataset of handwritten answers. The extensive experiments on a collection of handwritten answers from elementary school students, comprising 98,547 Japanese answers, demonstrate the superiority of the proposed method over the previous handwriting-recognition-based methods and its effectiveness for zero-shot automatic scoring, making it useful in low-resource settings. We also conduct ablation studies to evaluate the effects of different backbones and loss functions on the performance of the proposed scorer.
Keyword (in Japanese) (See Japanese page) 
(in English) Zero-Shot Automatic Scoring / Very Short Answers / Contrastive Learning Network / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 424, PRMU2025-58, pp. 90-95, March 2026.
Paper # PRMU2025-58 
Date of Issue 2026-03-17 (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)
Download PDF PRMU2025-58

Conference Information
Committee PRMU IPSJ-CVIM IBISML ITE-SIP  
Conference Date 2026-03-24 - 2026-03-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2026-03-PRMU-CVIM-IBISML-SIP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Contrastive Learning Network for Zero-Shot Automatic Scoring of Very Short Answers 
Sub Title (in English)  
Keyword(1) Zero-Shot Automatic Scoring  
Keyword(2) Very Short Answers  
Keyword(3) Contrastive Learning Network  
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1st Author's Name Tuan Nam Ly  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Hung Tuan Nguyen  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
3rd Author's Name Thanh-Nghia Truong  
3rd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
4th Author's Name Masaki Nakagawa  
4th Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2026-03-25 09:50:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2025-58 
Volume (vol) vol.125 
Number (no) no.424 
Page pp.90-95 
#Pages 6 
Date of Issue 2026-03-17 (PRMU) 

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