@inproceedings{xu-etal-2021-gradual,
title = "Gradual Fine-Tuning for Low-Resource Domain Adaptation",
author = "Xu, Haoran and
Ebner, Seth and
Yarmohammadi, Mahsa and
White, Aaron Steven and
Van Durme, Benjamin and
Murray, Kenton",
editor = "Ben-David, Eyal and
Cohen, Shay and
McDonald, Ryan and
Plank, Barbara and
Reichart, Roi and
Rotman, Guy and
Ziser, Yftah",
booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
month = apr,
year = "2021",
address = "Kyiv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.adaptnlp-1.22",
pages = "214--221",
abstract = "Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.",
}
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%0 Conference Proceedings
%T Gradual Fine-Tuning for Low-Resource Domain Adaptation
%A Xu, Haoran
%A Ebner, Seth
%A Yarmohammadi, Mahsa
%A White, Aaron Steven
%A Van Durme, Benjamin
%A Murray, Kenton
%Y Ben-David, Eyal
%Y Cohen, Shay
%Y McDonald, Ryan
%Y Plank, Barbara
%Y Reichart, Roi
%Y Rotman, Guy
%Y Ziser, Yftah
%S Proceedings of the Second Workshop on Domain Adaptation for NLP
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine
%F xu-etal-2021-gradual
%X Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.
%U https://aclanthology.org/2021.adaptnlp-1.22
%P 214-221
Markdown (Informal)
[Gradual Fine-Tuning for Low-Resource Domain Adaptation](https://aclanthology.org/2021.adaptnlp-1.22) (Xu et al., AdaptNLP 2021)
ACL
- Haoran Xu, Seth Ebner, Mahsa Yarmohammadi, Aaron Steven White, Benjamin Van Durme, and Kenton Murray. 2021. Gradual Fine-Tuning for Low-Resource Domain Adaptation. In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 214–221, Kyiv, Ukraine. Association for Computational Linguistics.