Deep neural networks excel at learning from labeled data and achieve
state-of-the-art resultson a wide array of Natural Language Processing tasks.
In contrast, learning from unlabeled data, especially under domain shift,
remains a challenge. Motivated by the latest advances, in this survey we review
neural unsupervised domain adaptation techniques which do not require labeled
target domain data. This is a more challenging yet a more widely applicable
setup. We outline methods, from early traditional non-neural methods to
pre-trained model transfer. We also revisit the notion of domain, and we
uncover a bias in the type of Natural Language Processing tasks which received
most attention. Lastly, we outline future directions, particularly the broader
need for out-of-distribution generalization of future NLP.

本篇综述针对最近的研究进展，对不需要标记目标领域数据的神经无监督领域自适应技术进行了概述，包括从传统非神经方法到预训练模型迁移的方法，并揭示了自然语言处理任务类型中存在的偏见问题以及未来的研究方向，特别是面向 NLP 的超分布通用化需求。