Abstract
Popular techniques for domain adaptation such as the feature augmentation method of Daumé III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such as those used in neural networks. In this paper, we describe simple neural extensions of these techniques. First, we propose a natural generalization of the feature augmentation method that uses K + 1 LSTMs where one model captures global patterns across all K domains and the remaining K models capture domain-specific information. Second, we propose a novel application of the framework for learning shared structures by Ando and Zhang (2005) to domain adaptation, and also provide a neural extension of their approach. In experiments on slot tagging over 17 domains, our methods give clear performance improvement over Daumé III (2009) applied on feature-rich CRFs.- Anthology ID:
- C16-1038
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 387–396
- Language:
- URL:
- https://aclanthology.org/C16-1038
- DOI:
- Cite (ACL):
- Young-Bum Kim, Karl Stratos, and Ruhi Sarikaya. 2016. Frustratingly Easy Neural Domain Adaptation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 387–396, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Frustratingly Easy Neural Domain Adaptation (Kim et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1038.pdf