@inproceedings{parvez-etal-2019-robust,
title = "Robust Text Classifier on Test-Time Budgets",
author = "Parvez, Md Rizwan and
Bolukbasi, Tolga and
Chang, Kai-Wei and
Saligrama, Venkatesh",
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/D19-1108",
doi = "10.18653/v1/D19-1108",
pages = "1167--1172",
abstract = "We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time. We take a different approach from existing methods and learn to dynamically filter a large fraction of unimportant words by a low-complexity selector such that any high-complexity state-of-art classifier only needs to process a small fraction of text, relevant for the target task. To this end, we propose a data aggregation method to train the classifier, allowing it to achieve competitive performance on fractured sentences. On four benchmark text classification tasks, we demonstrate that the framework gains consistent speedup with little degradation in accuracy on various selection budgets.",
}
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<abstract>We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time. We take a different approach from existing methods and learn to dynamically filter a large fraction of unimportant words by a low-complexity selector such that any high-complexity state-of-art classifier only needs to process a small fraction of text, relevant for the target task. To this end, we propose a data aggregation method to train the classifier, allowing it to achieve competitive performance on fractured sentences. On four benchmark text classification tasks, we demonstrate that the framework gains consistent speedup with little degradation in accuracy on various selection budgets.</abstract>
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%0 Conference Proceedings
%T Robust Text Classifier on Test-Time Budgets
%A Parvez, Md Rizwan
%A Bolukbasi, Tolga
%A Chang, Kai-Wei
%A Saligrama, Venkatesh
%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 parvez-etal-2019-robust
%X We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time. We take a different approach from existing methods and learn to dynamically filter a large fraction of unimportant words by a low-complexity selector such that any high-complexity state-of-art classifier only needs to process a small fraction of text, relevant for the target task. To this end, we propose a data aggregation method to train the classifier, allowing it to achieve competitive performance on fractured sentences. On four benchmark text classification tasks, we demonstrate that the framework gains consistent speedup with little degradation in accuracy on various selection budgets.
%R 10.18653/v1/D19-1108
%U https://aclanthology.org/D19-1108
%U https://doi.org/10.18653/v1/D19-1108
%P 1167-1172
Markdown (Informal)
[Robust Text Classifier on Test-Time Budgets](https://aclanthology.org/D19-1108) (Parvez et al., EMNLP-IJCNLP 2019)
ACL
- Md Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, and Venkatesh Saligrama. 2019. Robust Text Classifier on Test-Time Budgets. 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 1167–1172, Hong Kong, China. Association for Computational Linguistics.