Domain Adversarial Fine-Tuning as an Effective Regularizer
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In Natural Language Processing (NLP), pre-trained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. In this work, we extend the standard fine-tuning process of pretrained LMs by introducing a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. In-domain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularizer which prevents the model from overfitting to the task-specific domain. Empirical results on sentiment analysis, linguistic acceptability, and paraphrase detection show that AFTERleads to improved performance compared to standard fine-tuning.
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