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        <title>Leveraging Training Dynamics and Self-Training for Text Classification</title>
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            <namePart type="given">Yoav</namePart>
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    <abstract>The effectiveness of pre-trained language models in downstream tasks is highly dependent on the amount of labeled data available for training. Semi-supervised learning (SSL) is a promising technique that has seen wide attention recently due to its effectiveness in improving deep learning models when training data is scarce. Common approaches employ a teacher-student self-training framework, where a teacher network generates pseudo-labels for unlabeled data, which are then used to iteratively train a student network. In this paper, we propose a new self-training approach for text classification that leverages training dynamics of unlabeled data. We evaluate our approach on a wide range of text classification tasks, including emotion detection, sentiment analysis, question classification and gramaticality, which span a variety of domains, e.g, Reddit, Twitter, and online forums. Notably, our method is successful on all benchmarks, obtaining an average increase in F1 score of 3.5% over strong baselines in low resource settings.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.350</identifier>
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%0 Conference Proceedings
%T Leveraging Training Dynamics and Self-Training for Text Classification
%A Sosea, Tiberiu
%A Caragea, Cornelia
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sosea-caragea-2022-leveraging
%X The effectiveness of pre-trained language models in downstream tasks is highly dependent on the amount of labeled data available for training. Semi-supervised learning (SSL) is a promising technique that has seen wide attention recently due to its effectiveness in improving deep learning models when training data is scarce. Common approaches employ a teacher-student self-training framework, where a teacher network generates pseudo-labels for unlabeled data, which are then used to iteratively train a student network. In this paper, we propose a new self-training approach for text classification that leverages training dynamics of unlabeled data. We evaluate our approach on a wide range of text classification tasks, including emotion detection, sentiment analysis, question classification and gramaticality, which span a variety of domains, e.g, Reddit, Twitter, and online forums. Notably, our method is successful on all benchmarks, obtaining an average increase in F1 score of 3.5% over strong baselines in low resource settings.
%R 10.18653/v1/2022.findings-emnlp.350
%U https://aclanthology.org/2022.findings-emnlp.350
%U https://doi.org/10.18653/v1/2022.findings-emnlp.350
%P 4750-4762

Markdown (Informal)

[Leveraging Training Dynamics and Self-Training for Text Classification](https://aclanthology.org/2022.findings-emnlp.350) (Sosea & Caragea, Findings 2022)

• Leveraging Training Dynamics and Self-Training for Text Classification (Sosea & Caragea, Findings 2022)

ACL

• Tiberiu Sosea and Cornelia Caragea. 2022. Leveraging Training Dynamics and Self-Training for Text Classification. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4750–4762, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.