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Question generation has recently shown impressive results in customizingquestion answering (QA) systems to new domains. These approaches circumvent theneed for manually annotated training data from the new domain and, instead,generate synthetic question-answer pairs that are used for training. However,existing methods for question generation rely on large amounts of syntheticallygenerated datasets and costly computational resources, which render thesetechniques widely inaccessible when the text corpora is of limited size. Thisis problematic as many niche domains rely on small text corpora, whichnaturally restricts the amount of synthetic data that can be generated. In thispaper, we propose a novel framework for domain adaptation called contrastivedomain adaptation for QA (CAQA). Specifically, CAQA combines techniques fromquestion generation and domain-invariant learning to answer out-of-domainquestions in settings with limited text corpora. Here, we train a QA system onboth source data and generated data from the target domain with a contrastiveadaptation loss that is incorporated in the training objective. By combiningtechniques from question generation and domain-invariant learning, our modelachieved considerable improvements compared to state-of-the-art baselines.

Contrastive Domain Adaptation for Question Answering using Limited Text Corpora