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Paper Abstract and Keywords
Presentation 2017-03-02 09:00
[Poster Presentation] Use of the end of sentence and speaker-derived information in recurrent neural network language models for multiparty conversations.
Hiroto Ashikawa, Naohiro Tawara (Waseda Univ.), Atsunori Ogawa, Tomoharu Iwata (NTT), Tetsuji Ogawa, Tetsunori Kobayashi (Waseda Univ.) EA2016-133 SIP2016-188 SP2016-128
Abstract (in Japanese) (See Japanese page) 
(in English) Information on the end of sentence (EOS) and speaker alternation was exploited in recurrent neural network-based language models and its contribution to improvement in performance of predicting subsequent words
in multiparty conversations was investigated.
These kinds of information were represented as context cues and feature vectors.
The former context cues can be inserted to the transcriptions for training, equivalently as other word tokens.
The latter feature vectors can be taken as inputs to the neural networks. Experimental comparisons using actual multiparty conversations demonstrated that both representations reduced the perplexity compared to
the case without the EOS and speaker information.
The EOS information contributed a lot to improvement in performance of word prediction, especially for multiparty conversations.
Keyword (in Japanese) (See Japanese page) 
(in English) recurrent neural network / context cue / language model / multiparty conversation / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 477, SP2016-128, pp. 287-290, March 2017.
Paper # SP2016-128 
Date of Issue 2017-02-22 (EA, SIP, SP) 
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)
Download PDF EA2016-133 SIP2016-188 SP2016-128

Conference Information
Committee SP SIP EA  
Conference Date 2017-03-01 - 2017-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics 
Paper Information
Registration To SP 
Conference Code 2017-03-SP-SIP-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Use of the end of sentence and speaker-derived information in recurrent neural network language models for multiparty conversations. 
Sub Title (in English)  
Keyword(1) recurrent neural network  
Keyword(2) context cue  
Keyword(3) language model  
Keyword(4) multiparty conversation  
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1st Author's Name Hiroto Ashikawa  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Naohiro Tawara  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Atsunori Ogawa  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Tomoharu Iwata  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Tetsuji Ogawa  
5th Author's Affiliation Waseda University (Waseda Univ.)
6th Author's Name Tetsunori Kobayashi  
6th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2017-03-02 09:00:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2016-133, SIP2016-188, SP2016-128 
Volume (vol) vol.116 
Number (no) no.475(EA), no.476(SIP), no.477(SP) 
Page pp.287-290 
#Pages 4 
Date of Issue 2017-02-22 (EA, SIP, SP) 

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