@inproceedings{zhu-etal-2021-combining,
title = "Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification",
author = "Zhu, Yi and
Shareghi, Ehsan and
Li, Yingzhen and
Reichart, Roi and
Korhonen, Anna",
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.76",
doi = "10.18653/v1/2021.eacl-main.76",
pages = "894--908",
abstract = "Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled data shortage. To bridge this gap, we combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. Compared to strong supervised learning baselines, our semi-supervised classification framework is highly competitive and outperforms the state-of-the-art counterparts in low-resource settings across several languages.",
}
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<abstract>Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled data shortage. To bridge this gap, we combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. Compared to strong supervised learning baselines, our semi-supervised classification framework is highly competitive and outperforms the state-of-the-art counterparts in low-resource settings across several languages.</abstract>
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%0 Conference Proceedings
%T Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
%A Zhu, Yi
%A Shareghi, Ehsan
%A Li, Yingzhen
%A Reichart, Roi
%A Korhonen, Anna
%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 zhu-etal-2021-combining
%X Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled data shortage. To bridge this gap, we combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. Compared to strong supervised learning baselines, our semi-supervised classification framework is highly competitive and outperforms the state-of-the-art counterparts in low-resource settings across several languages.
%R 10.18653/v1/2021.eacl-main.76
%U https://aclanthology.org/2021.eacl-main.76
%U https://doi.org/10.18653/v1/2021.eacl-main.76
%P 894-908
Markdown (Informal)
[Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification](https://aclanthology.org/2021.eacl-main.76) (Zhu et al., EACL 2021)
ACL