Question generation has recently shown impressive results in customizing
question answering (QA) systems to new domains. These approaches circumvent the
need 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 synthetically
generated datasets and costly computational resources, which render these
techniques widely inaccessible when the text corpora is of limited size. This
is problematic as many niche domains rely on small text corpora, which
naturally restricts the amount of synthetic data that can be generated. In this
paper, we propose a novel framework for domain adaptation called contrastive
domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from
question generation and domain-invariant learning to answer out-of-domain
questions in settings with limited text corpora. Here, we train a QA system on
both source data and generated data from the target domain with a contrastive
adaptation loss that is incorporated in the training objective. By combining
techniques from question generation and domain-invariant learning, our model
achieved considerable improvements compared to state-of-the-art baselines.

本研究提出了一种名为对比领域自适应问答（CAQA）的新型领域适应框架，该框架结合了问题生成和域不变学习技术，用于在文本语料库受限的情况下回答域外问题，并且相较于现有技术取得了显著的效果改善。