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Paper Abstract and Keywords
Presentation 2023-03-01 10:30
A Study of Utterance Generation Models based on TV Programs and Interests
Yuta Hagio, Makoto Okuda, Marina Kamimura, Yutaka Kaneko, Hisayuki Ohmata (NHK) BioX2022-64 CNR2022-30
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we discuss the results of a study of an utterance generation model based on TV programs and specifiedinterest words, with the aim of implementing it in a companion robot that watches TV with humans. In order to automaticallygenerate the robot’s utterances while watching TV, we created a TV program-related utterance dataset by adding about 12,000robot utterances to the program’s captions. Using this dataset, we built an utterance generation model by fine-tuning a large-scalegeneral-purpose language model for chat dialog. Our model can generate utterances with the given specified interest wordas the topic, taking into account the context of the TV program using its captions and the interest word. As a result of evaluationof the sentences generated by our model, we confirmed that about 88% of the sentences are natural Japanese sentences, andabout 75% of the sentences are natural sentences in the context of the program. In addition, about 83% of the utterancesreflected the specified interest words. It was found that our model can generate utterances that are natural in the context of theprogram’s captions and that suggest interest in the specified interest words.
Keyword (in Japanese) (See Japanese page) 
(in English) Companion robot / TV-viewing / Utterance generation / Caption data / Interest words / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 395, CNR2022-30, pp. 13-18, March 2023.
Paper # CNR2022-30 
Date of Issue 2023-02-22 (BioX, CNR) 
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 BioX2022-64 CNR2022-30

Conference Information
Committee CNR BioX  
Conference Date 2023-03-01 - 2023-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CNR 
Conference Code 2023-03-CNR-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Utterance Generation Models based on TV Programs and Interests 
Sub Title (in English)  
Keyword(1) Companion robot  
Keyword(2) TV-viewing  
Keyword(3) Utterance generation  
Keyword(4) Caption data  
Keyword(5) Interest words  
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1st Author's Name Yuta Hagio  
1st Author's Affiliation Japan Broadcasting Corporation (NHK)
2nd Author's Name Makoto Okuda  
2nd Author's Affiliation Japan Broadcasting Corporation (NHK)
3rd Author's Name Marina Kamimura  
3rd Author's Affiliation Japan Broadcasting Corporation (NHK)
4th Author's Name Yutaka Kaneko  
4th Author's Affiliation Japan Broadcasting Corporation (NHK)
5th Author's Name Hisayuki Ohmata  
5th Author's Affiliation Japan Broadcasting Corporation (NHK)
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Speaker Author-1 
Date Time 2023-03-01 10:30:00 
Presentation Time 30 minutes 
Registration for CNR 
Paper # BioX2022-64, CNR2022-30 
Volume (vol) vol.122 
Number (no) no.394(BioX), no.395(CNR) 
Page pp.13-18 
#Pages 6 
Date of Issue 2023-02-22 (BioX, CNR) 

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