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| Presentation | 2021-09-18 16:05 [Keynote Address] Neural Network as an Explainable Human -- A New Approach to Contrastive Studies -- Yugo Murawaki (Kyoto Univ.)  TL2021-16 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this talk, I argue that techniques developed in the field of explainable AI (XAI) have potential applications in computational human sciences. In typical XAI scenarios, artificial intelligence is seen as a technological Other that is obliged to win human trust by explaining the black box. In computational human sciences, however, it is humans that are the black box, and artificial intelligence serves as an approximation of human functions. This observation motivates us to use explanation methods to explain humans. As a concrete example, I show that neural network-based classifiers can be applied to contrastive studies. We begin by training a classifier to discriminate texts written by two groups of humans and then apply an explanation method to analyze how it performs classification. A major advantage of this approach is that the high expressive power of modern neural networks allows us to investigate context-sensitive words and long expressions in an explorative manner. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | explainable AI / neural networks / classifiers / contrastive studies / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 180, TL2021-16, pp. 23-27, Sept. 2021. | |
| Paper # | TL2021-16 | |
| Date of Issue | 2021-09-11 (TL) | |
| 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 | TL2021-16 |
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| Conference Information | |
| Committee | TL |
| Conference Date | 2021-09-18 - 2021-09-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Language Processing and Language Learning |
| Paper Information | |
| Registration To | TL |
| Conference Code | 2021-09-TL |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Neural Network as an Explainable Human |
| Sub Title (in English) | A New Approach to Contrastive Studies |
| Keyword(1) | explainable AI |
| Keyword(2) | neural networks |
| Keyword(3) | classifiers |
| Keyword(4) | contrastive studies |
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| 1st Author's Name | Yugo Murawaki |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-09-18 16:05:00 |
| Presentation Time | 60 minutes |
| Registration for | TL |
| Paper # | TL2021-16 |
| Volume (vol) | vol.121 |
| Number (no) | no.180 |
| Page | pp.23-27 |
| #Pages | 5 |
| Date of Issue | 2021-09-11 (TL) |
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