SOURCE-FREE UNSUPERVISED DOMAIN ADAPTATION FOR QUESTION ANSWERING

Based on the assumption that samples in the source and target domains are freely accessible during training, unsupervised domain adaptation (UDA) of question answering (QA) aims to transfer knowledge learned from labeled source datasets to similar tasks in the unlabeled target domains. However, such assumption can easily lead to privacy violation issues in real-world applications, especially when the source domain data involves privacy-intensive domains such as finance and healthcare. In this paper, we introduce Source-Free Domain Adaptation Framework for QA (denoted as SFQA), which only allows access to trained source models for target learning, making data privacy protection more promising. Specifically, the proposed SFQA model consists of a feature extractor module (Bert Encoder) and a classifier module (Answer Classifier). We first transfer the trained source model to the target model while keeping the source classifier module frozen. Then we adopt the question generation model to generate questions and answers for the target domain. Taking the generated question and target domain context as input, and the generated answers as pseudo-labels, we train the target model with joint entropy to learn target domain-specific feature extractors. The experimental results demonstrate the superiority and effectiveness of the proposed SFQA, and show that SFQA outperforms the state-of-the-art methods.