@inproceedings{wei-etal-2021-text,
title = "Text Augmentation in a Multi-Task View",
author = "Wei, Jason and
Huang, Chengyu and
Xu, Shiqi and
Vosoughi, Soroush",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.252",
doi = "10.18653/v1/2021.eacl-main.252",
pages = "2888--2894",
abstract = "Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective{---}a multi-task view (MTV) of data augmentation{---}in which the primary task trains on original examples and the auxiliary task trains on augmented examples. In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger augmentation functions. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that using the MTV leads to higher and more robust performance than traditional augmentation.",
}
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<abstract>Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective—a multi-task view (MTV) of data augmentation—in which the primary task trains on original examples and the auxiliary task trains on augmented examples. In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger augmentation functions. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that using the MTV leads to higher and more robust performance than traditional augmentation.</abstract>
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%0 Conference Proceedings
%T Text Augmentation in a Multi-Task View
%A Wei, Jason
%A Huang, Chengyu
%A Xu, Shiqi
%A Vosoughi, Soroush
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F wei-etal-2021-text
%X Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective—a multi-task view (MTV) of data augmentation—in which the primary task trains on original examples and the auxiliary task trains on augmented examples. In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger augmentation functions. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that using the MTV leads to higher and more robust performance than traditional augmentation.
%R 10.18653/v1/2021.eacl-main.252
%U https://aclanthology.org/2021.eacl-main.252
%U https://doi.org/10.18653/v1/2021.eacl-main.252
%P 2888-2894
Markdown (Informal)
[Text Augmentation in a Multi-Task View](https://aclanthology.org/2021.eacl-main.252) (Wei et al., EACL 2021)
ACL
- Jason Wei, Chengyu Huang, Shiqi Xu, and Soroush Vosoughi. 2021. Text Augmentation in a Multi-Task View. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2888–2894, Online. Association for Computational Linguistics.