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.- Anthology ID:
- 2020.coling-main.603
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6838–6855
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.603
- DOI:
- 10.18653/v1/2020.coling-main.603
- Cite (ACL):
- Alan Ramponi and Barbara Plank. 2020. Neural Unsupervised Domain Adaptation in NLP—A Survey. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6838–6855, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Neural Unsupervised Domain Adaptation in NLP—A Survey (Ramponi & Plank, COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.603.pdf
- Code
- bplank/awesome-neural-adaptation-in-NLP
- Data
- Penn Treebank