Neural Unsupervised Domain Adaptation in NLP—A Survey

Alan Ramponi, Barbara Plank

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstract

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.
OriginalsprogEngelsk
TitelThe 28th International Conference on Computational Linguistics
ForlagAssociation for Computational Linguistics
Publikationsdatodec. 2020
StatusUdgivet - dec. 2020

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