%0 Conference Proceedings %T Neural Unsupervised Domain Adaptation in NLP—A Survey %A Ramponi, Alan %A Plank, Barbara %Y Scott, Donia %Y Bel, Nuria %Y Zong, Chengqing %S Proceedings of the 28th International Conference on Computational Linguistics %D 2020 %8 December %I International Committee on Computational Linguistics %C Barcelona, Spain (Online) %F ramponi-plank-2020-neural %X 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. %R 10.18653/v1/2020.coling-main.603 %U https://aclanthology.org/2020.coling-main.603 %U https://doi.org/10.18653/v1/2020.coling-main.603 %P 6838-6855