@inproceedings{choi-etal-2020-less,
title = "Less is More: Attention Supervision with Counterfactuals for Text Classification",
author = "Choi, Seungtaek and
Park, Haeju and
Yeo, Jinyoung and
Hwang, Seung-won",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.543",
doi = "10.18653/v1/2020.emnlp-main.543",
pages = "6695--6704",
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.",
}
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%0 Conference Proceedings
%T Less is More: Attention Supervision with Counterfactuals for Text Classification
%A Choi, Seungtaek
%A Park, Haeju
%A Yeo, Jinyoung
%A Hwang, Seung-won
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F choi-etal-2020-less
%X 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.
%R 10.18653/v1/2020.emnlp-main.543
%U https://aclanthology.org/2020.emnlp-main.543
%U https://doi.org/10.18653/v1/2020.emnlp-main.543
%P 6695-6704
Markdown (Informal)
[Less is More: Attention Supervision with Counterfactuals for Text Classification](https://aclanthology.org/2020.emnlp-main.543) (Choi et al., EMNLP 2020)
ACL