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        <title>Variational Pretraining for Semi-supervised Text Classification</title>
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        <namePart type="given">Suchin</namePart>
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    <abstract>We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier. Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. We also find that fine-tuning to in-domain data is crucial to achieving decent performance from contextual embeddings when working with limited supervision. We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.</abstract>
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%0 Conference Proceedings
%T Variational Pretraining for Semi-supervised Text Classification
%A Gururangan, Suchin
%A Dang, Tam
%A Card, Dallas
%A Smith, Noah A.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F gururangan-etal-2019-variational
%X We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier. Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. We also find that fine-tuning to in-domain data is crucial to achieving decent performance from contextual embeddings when working with limited supervision. We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.
%R 10.18653/v1/P19-1590
%U https://aclanthology.org/P19-1590
%U https://doi.org/10.18653/v1/P19-1590
%P 5880-5894

Markdown (Informal)

[Variational Pretraining for Semi-supervised Text Classification](https://aclanthology.org/P19-1590) (Gururangan et al., ACL 2019)

• Variational Pretraining for Semi-supervised Text Classification (Gururangan et al., ACL 2019)

ACL

• Suchin Gururangan, Tam Dang, Dallas Card, and Noah A. Smith. 2019. Variational Pretraining for Semi-supervised Text Classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5880–5894, Florence, Italy. Association for Computational Linguistics.