Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval
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In this paper, we propose a new domain adaptation method called back-training, a superior alternative to self-training. While self-training results in synthetic training data of the form quality inputs aligned with noisy outputs, back-training results in noisy inputs aligned with quality outputs. Our experimental results on unsupervised domain adaptation of question generation and passage retrieval models from Natural Questions domain to the machine learning domain show that back-training outperforms self-training by a large margin: 9.3 BLEU-1 points on generation, and 7.9 accuracy points on top-1 retrieval. We release MLQuestions, a domain-adaptation dataset for the machine learning domain containing 50K unaligned passages and 35K unaligned questions, and 3K aligned passage and question pairs. Our data and code are available at https://github.com/McGill-NLP/MLQuestions
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