Domain Adversarial Fine-Tuning as an Effective Regularizer
Giorgos Vernikos, Katerina Margatina, Alexandra Chronopoulou, Ion Androutsopoulos · 2020
In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results.However, standard fine-tuning can degrade the general-domain representations captured during pretraining.To address this issue, we introduce 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.Indomain 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 various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.