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        <title>MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification</title>
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        <namePart type="given">Jiaao</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data. By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at https://github.com/GT-SALT/MixText.</abstract>
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%0 Conference Proceedings
%T MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
%A Chen, Jiaao
%A Yang, Zichao
%A Yang, Diyi
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-mixtext
%X This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data. By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at https://github.com/GT-SALT/MixText.
%R 10.18653/v1/2020.acl-main.194
%U https://aclanthology.org/2020.acl-main.194
%U https://doi.org/10.18653/v1/2020.acl-main.194
%P 2147-2157

Markdown (Informal)

[MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://aclanthology.org/2020.acl-main.194) (Chen et al., ACL 2020)

• MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (Chen et al., ACL 2020)

ACL

• Jiaao Chen, Zichao Yang, and Diyi Yang. 2020. MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2147–2157, Online. Association for Computational Linguistics.