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| Paper Abstract and Keywords | ||
| Presentation | 2016-12-20 09:55 Hard-Attentional Neural Network Models for Emphasis Speech Translation Quoc Truong Do, Sakriani Sakti, Graham Neubig, Satoshi Nakamura (NAIST)  SP2016-50 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Traditional speech translation systems are oblivious to paralinguistic information. A recent work has tried to tackle this task by utilizing conditional random fields (CRFs). Although CRFs allow for consideration of rich features and local context, they have difficulty in handling continuous variables, and cannot capture long-distance dependencies easily. In this paper, we propose a new model for emphasis transfer in speech translation using an approach based on neural networks. Our experiments showed a significant improvement of the proposed model over the previous model by 4% target-language emphasis prediction F-measure according to objective evaluation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Emphasis translation / speech translation / attentional network model / hard-attentional / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 378, SP2016-50, pp. 7-8, Dec. 2016. | |
| Paper # | SP2016-50 | |
| Date of Issue | 2016-12-13 (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) | |
| Notes on Review | This article is a technical report without peer review, and its polished version will be published elsewhere. | |
| Download PDF | SP2016-50 |
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| Conference Information | |
| Committee | SP IPSJ-SLP NLC IPSJ-NL |
| Conference Date | 2016-12-20 - 2016-12-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | NTT Musashino R&D |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The 18th Spoken Language Symposium & The Third Natural Language Processing Symposium |
| Paper Information | |
| Registration To | SP |
| Conference Code | 2016-12-SP-SLP-NLC-NL |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hard-Attentional Neural Network Models for Emphasis Speech Translation |
| Sub Title (in English) | |
| Keyword(1) | Emphasis translation |
| Keyword(2) | speech translation |
| Keyword(3) | attentional network model |
| Keyword(4) | hard-attentional |
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| Keyword(8) | |
| 1st Author's Name | Quoc Truong Do |
| 1st Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 2nd Author's Name | Sakriani Sakti |
| 2nd Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 3rd Author's Name | Graham Neubig |
| 3rd Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 4th Author's Name | Satoshi Nakamura |
| 4th Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
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| Speaker | Author-1 |
| Date Time | 2016-12-20 09:55:00 |
| Presentation Time | 25 minutes |
| Registration for | SP |
| Paper # | SP2016-50 |
| Volume (vol) | vol.116 |
| Number (no) | no.378 |
| Page | pp.7-8 |
| #Pages | 2 |
| Date of Issue | 2016-12-13 (SP) |
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