Frustratingly Easy Neural Domain Adaptation

Young-Bum Kim, Karl Stratos, Ruhi Sarikaya


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/C16-1038.pdf