Neural Unsupervised Domain Adaptation in NLP—A Survey
Alan Ramponi, Barbara Plank · 2020
Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on 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. 1