Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Alan Ramponi, Barbara Plank

Publisher name:  International Committee on Computational Linguistics

DOI:  10.18653/v1/2020.coling-main.603

Volume Information:  Pages: 6838–6855

Paper Link:   https://aclanthology.org/2020.coling-main.603/