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| Presentation | 2019-02-07 14:45 Call Scene Segmentation based on Neural Networks with Conversational Contexts Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Hosana Kamiyama, Takanobu Oba, Yushi Aono (NTT)  NLC2018-39 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Call scene segmentation that automatically splits contact center dialogues into several call scenes is useful for constructing operator assist systems. This paper develops call scene segmentation methods based on supervised training by defining call scenes in contact center dialogues as following five tag types: opening, call reason, response, customer confirmation, and closing. It is important to capture conversational contexts between an operator and a customer in the contact center dialogues for performing precise online call scene segmentation. Therefore, this paper proposes neural call scene segmentation methods that can directly estimate a call scene label sequence from an utterance sequence based on sequential labeling approach. The proposed methods can capture long-range conversational contexts by simultaneously dealing with both the sequence of sentences and the sequence of speaker role labels. An experiment using contact center dialogue data sets demonstrates the effectiveness of the proposed methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Call scene segmentation / contact center / sequential labeling / neural networks / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 439, NLC2018-39, pp. 21-26, Feb. 2019. | |
| Paper # | NLC2018-39 | |
| Date of Issue | 2019-01-31 (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 | NLC2018-39 |
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| Conference Information | |
| Committee | NLC IPSJ-IFAT |
| Conference Date | 2019-02-07 - 2019-02-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Ryukoku University Omiya Campus |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The 14th Text Analytics Symposium |
| Paper Information | |
| Registration To | NLC |
| Conference Code | 2019-02-NLC-IFAT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Call Scene Segmentation based on Neural Networks with Conversational Contexts |
| Sub Title (in English) | |
| Keyword(1) | Call scene segmentation |
| Keyword(2) | contact center |
| Keyword(3) | sequential labeling |
| Keyword(4) | neural networks |
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| 1st Author's Name | Ryo Masumura |
| 1st Author's Affiliation | NTT Corporation (NTT) |
| 2nd Author's Name | Tomohiro Tanaka |
| 2nd Author's Affiliation | NTT Corporation (NTT) |
| 3rd Author's Name | Atsushi Ando |
| 3rd Author's Affiliation | NTT Corporation (NTT) |
| 4th Author's Name | Hosana Kamiyama |
| 4th Author's Affiliation | NTT Corporation (NTT) |
| 5th Author's Name | Takanobu Oba |
| 5th Author's Affiliation | NTT Corporation (NTT) |
| 6th Author's Name | Yushi Aono |
| 6th Author's Affiliation | NTT Corporation (NTT) |
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| Speaker | Author-1 |
| Date Time | 2019-02-07 14:45:00 |
| Presentation Time | 25 minutes |
| Registration for | NLC |
| Paper # | NLC2018-39 |
| Volume (vol) | vol.118 |
| Number (no) | no.439 |
| Page | pp.21-26 |
| #Pages | 6 |
| Date of Issue | 2019-01-31 (NLC) |
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