@inproceedings{lertvittayakumjorn-toni-2019-human,
title = "Human-grounded Evaluations of Explanation Methods for Text Classification",
author = "Lertvittayakumjorn, Piyawat and
Toni, Francesca",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1523",
doi = "10.18653/v1/D19-1523",
pages = "5195--5205",
abstract = "Due to the black-box nature of deep learning models, methods for explaining the models{'} results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions. The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose.",
}
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%0 Conference Proceedings
%T Human-grounded Evaluations of Explanation Methods for Text Classification
%A Lertvittayakumjorn, Piyawat
%A Toni, Francesca
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lertvittayakumjorn-toni-2019-human
%X Due to the black-box nature of deep learning models, methods for explaining the models’ results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions. The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose.
%R 10.18653/v1/D19-1523
%U https://aclanthology.org/D19-1523
%U https://doi.org/10.18653/v1/D19-1523
%P 5195-5205
Markdown (Informal)
[Human-grounded Evaluations of Explanation Methods for Text Classification](https://aclanthology.org/D19-1523) (Lertvittayakumjorn & Toni, EMNLP-IJCNLP 2019)
ACL
- Piyawat Lertvittayakumjorn and Francesca Toni. 2019. Human-grounded Evaluations of Explanation Methods for Text Classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5195–5205, Hong Kong, China. Association for Computational Linguistics.