Frustratingly Easy Neural Domain Adaptation
Popular techniques for domain adaptation such as the feature augmentation method of Daume 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 Daume III (2009) applied on feature rich CRFs.
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