Abstract
We aim to leverage human and machine intelligence together for attention supervision. Specifically, we show that human annotation cost can be kept reasonably low, while its quality can be enhanced by machine self-supervision. Specifically, for this goal, we explore the advantage of counterfactual reasoning, over associative reasoning typically used in attention supervision. Our empirical results show that this machine-augmented human attention supervision is more effective than existing methods requiring a higher annotation cost, in text classification tasks, including sentiment analysis and news categorization.
| Original language | English |
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| Title of host publication | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 6695-6704 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781952148606 |
| Publication status | Published - 2020 |
| Event | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online Duration: 2020 Nov 16 → 2020 Nov 20 |
Publication series
| Name | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
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Conference
| Conference | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 |
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| City | Virtual, Online |
| Period | 20/11/16 → 20/11/20 |
Bibliographical note
Funding Information:This work is supported by AI Graduate School Program (2020-0-01361) and IITP grant (No.2017-0-01779, XAI) supervised by IITP. Hwang is a corresponding author.
Publisher Copyright:
© 2020 Association for Computational Linguistics
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics