Gradual Fine-Tuning for Low-Resource Domain Adaptation

Haoran Xu; Seth Ebner; Mahsa Yarmohammadi; Aaron Steven White; Benjamin Van Durme; Kenton Murray

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arXiv · 2103.02205

Gradual Fine-Tuning for Low-Resource Domain Adaptation

Haoran Xu·Seth Ebner·Mahsa Yarmohammadi·Aaron Steven White·Benjamin Van Durme·Kenton Murray

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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-stage process can yield substantial further gains and can be applied without modifying the model or learning objective.

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Haoran Xu, Seth Ebner, Mahsa Yarmohammadi, Aaron Steven White, Benjamin Van Durme, Kenton Murray. 2021-03-03. Gradual Fine-Tuning for Low-Resource Domain Adaptation. https://arxiv.org/abs/2103.02205

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