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Paper Abstract and Keywords
Presentation 2023-03-18 15:40
An Experimental Analysis of Sub-tasks for Multi-task Learning-based Text Classification
Yusuke Kimura (Doshisha Univ.), Takahiro Komamizu (Nagoya Univ.), Kenji Hatano (Doshisha Univ.) NLC2022-26
Abstract (in Japanese) (See Japanese page) 
(in English) Multi-task learning, in which a phrase extraction such as key phrase extraction and named entity extraction is chosen for a sub-task, has shown improvements for the accuracy of text classification. However, multi-task learning requires extra annotations for sub-tasks in addition to the annotation for the mainstream text classification task. That extra annotation incurs extra human and financial costs, which are typically not small. These costs are therefore a barrier to the practical application of multi-task learning. To deal with this issue, the authors conducted research on realizing sub-tasks by automatic labeling for sub-tasks using a heuristic method, and confirmed that it improved classification accuracy. Although the previous findings can be widely applicable to other heuristic-based labeling methods to create sub-tasks, their availability has not been discussed. In addition, the requirements that such sub-tasks should satisfy have not been revealed. Therefore, this study attemps to clarify the requirements for improving the accuracy of text classification by using generic subtasks in multi-task learning.
Keyword (in Japanese) (See Japanese page) 
(in English) text classification / Subword-phrase / Sub-task / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 449, NLC2022-26, pp. 38-43, March 2023.
Paper # NLC2022-26 
Date of Issue 2023-03-11 (NLC) 
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 NLC2022-26

Conference Information
Committee NLC IPSJ-NL  
Conference Date 2023-03-18 - 2023-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) OIST 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Applications of natural language processing, and etc. 
Paper Information
Registration To NLC 
Conference Code 2023-03-NLC-NL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Experimental Analysis of Sub-tasks for Multi-task Learning-based Text Classification 
Sub Title (in English)  
Keyword(1) text classification  
Keyword(2) Subword-phrase  
Keyword(3) Sub-task  
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1st Author's Name Yusuke Kimura  
1st Author's Affiliation Doshisha University (Doshisha Univ.)
2nd Author's Name Takahiro Komamizu  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Kenji Hatano  
3rd Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker Author-1 
Date Time 2023-03-18 15:40:00 
Presentation Time 20 minutes 
Registration for NLC 
Paper # NLC2022-26 
Volume (vol) vol.122 
Number (no) no.449 
Page pp.38-43 
#Pages 6 
Date of Issue 2023-03-11 (NLC) 

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