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Paper Abstract and Keywords
Presentation 2020-01-26 11:40
Modeling for Infant Vocabulary Acquisition System using Deep Reinforcement Learning
Masaki Taguchi, Yasuhiro Minami (UEC) HCS2019-73
Abstract (in Japanese) (See Japanese page) 
(in English) We propose an infant vocabulary acquisition model that identifies psychological infant vocabulary development findings (joint attention, learning bias, and understanding intention) associated with symbol grounding using deep reinforcement learning. In deep reinforcement learning, we use DDQN and LSTM, which treats the time sequence data of long-term dependency. We use the features obtained from real objects to ground words to those objects. Simulation experiments investigated the symbol-grounding abilities of the model and the appearances of psychological findings in the process of infant word acquisition. We confirmed that our proposed model can ground words to the objects and achieved joint attention and understanding intention. We also confirmed that it acquires (by learning) noun bias, which is thought to innately exist by many psychologists. These results confirm that the multiple psychological phenomena of language acquisition can be modeled using the latest neural network.
Keyword (in Japanese) (See Japanese page) 
(in English) double deep q-network / long short-term memory / image recognition / feature extraction / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 394, HCS2019-73, pp. 111-116, Jan. 2020.
Paper # HCS2019-73 
Date of Issue 2020-01-18 (HCS) 
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 HCS2019-73

Conference Information
Committee HCS  
Conference Date 2020-01-25 - 2020-01-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Room407, J:COM HorutoHall OITA (Oita) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Psychology and Life-stage of Communication, etc. 
Paper Information
Registration To HCS 
Conference Code 2020-01-HCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Modeling for Infant Vocabulary Acquisition System using Deep Reinforcement Learning 
Sub Title (in English)  
Keyword(1) double deep q-network  
Keyword(2) long short-term memory  
Keyword(3) image recognition  
Keyword(4) feature extraction  
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1st Author's Name Masaki Taguchi  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Yasuhiro Minami  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker
Date Time 2020-01-26 11:40:00 
Presentation Time 20 
Registration for HCS 
Paper # IEICE-HCS2019-73 
Volume (vol) IEICE-119 
Number (no) no.394 
Page pp.111-116 
#Pages IEICE-6 
Date of Issue IEICE-HCS-2020-01-18 


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