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Paper Abstract and Keywords
Presentation 2022-05-27 15:28
[Poster Presentation] Improving the accuracy of machine translation of sentences including scientific terms
Kaoru Kato, Paik Incheon (Aizu Univ.) SC2022-14
Abstract (in Japanese) (See Japanese page) 
(in English) Machine translation has long been a subject of research as a method of translation. In the past, dictionary-based machine translation and machine translation using statistical models were the mainstream methods, but today, neural machine translation using neural networks is the mainstream method. However, current machine translation methods sometimes produce mistranslations or unnatural translations when translating texts containing technical terms. Therefore, we focused on the field of science and aimed to improve the translation accuracy of sentences containing scientific terminology. We prepared a dataset of approximately 3 million common Japanese-English bilingual corpora and 120,000 Japanese-English abstracts of scientific papers from NTCIR. We used Transformer model for the architecture. The BLEU score used to evaluate machine translation performance was about 19.3 for the model trained on common data only, and 20.9 for the model trained on the abstracts of additional scientific papers, indicating an improvement in performance. Learning additional sentences in the field in which translation accuracy is to be improved was an effective method for improving the translation accuracy of sentences containing technical terms.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Translation / NLP / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 50, SC2022-14, pp. 83-87, May 2022.
Paper # SC2022-14 
Date of Issue 2022-05-20 (SC) 
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 SC  
Conference Date 2022-05-27 - 2022-05-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AI Service and Digital Transformation, and general topics 
Paper Information
Registration To SC 
Conference Code 2022-05-SC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving the accuracy of machine translation of sentences including scientific terms 
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Keyword(1) Machine Translation  
Keyword(2) NLP  
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1st Author's Name Kaoru Kato  
1st Author's Affiliation University of Aizu (Aizu Univ.)
2nd Author's Name Paik Incheon  
2nd Author's Affiliation University of Aizu (Aizu Univ.)
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Speaker Author-1 
Date Time 2022-05-27 15:28:00 
Presentation Time 3 minutes 
Registration for SC 
Paper # SC2022-14 
Volume (vol) vol.122 
Number (no) no.50 
Page pp.83-87 
#Pages 5 
Date of Issue 2022-05-20 (SC) 

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