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Deep neural networks excel at learning from labeled data and achievestate-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 reviewneural unsupervised domain adaptation techniques which do not require labeledtarget domain data. This is a more challenging yet a more widely applicablesetup. We outline methods, from early traditional non-neural methods topre-trained model transfer. We also revisit the notion of domain, and weuncover a bias in the type of Natural Language Processing tasks which receivedmost attention. Lastly, we outline future directions, particularly the broaderneed for out-of-distribution generalization of future NLP.

Neural Unsupervised Domain Adaptation in NLP---A Survey