Gradual Fine-Tuning for Low-Resource Domain Adaptation

03/03/2021 ∙ by Haoran Xu, et al. ∙ 0

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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