@inproceedings{kim-etal-2016-frustratingly,
title = "Frustratingly Easy Neural Domain Adaptation",
author = "Kim, Young-Bum and
Stratos, Karl and
Sarikaya, Ruhi",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1038",
pages = "387--396",
abstract = "Popular techniques for domain adaptation such as the feature augmentation method of Daum{\'e} 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 Daum{\'e} III (2009) applied on feature-rich CRFs.",
}
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%0 Conference Proceedings
%T Frustratingly Easy Neural Domain Adaptation
%A Kim, Young-Bum
%A Stratos, Karl
%A Sarikaya, Ruhi
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F kim-etal-2016-frustratingly
%X 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.
%U https://aclanthology.org/C16-1038
%P 387-396
Markdown (Informal)
[Frustratingly Easy Neural Domain Adaptation](https://aclanthology.org/C16-1038) (Kim et al., COLING 2016)
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.