Frustratingly easy semi-supervised domain adaptation

In this work, we propose a semisupervised extension to a well-known supervised domain adaptation approach (EA) (Daumé III, 2007). Our proposed approach (EA++) builds on the notion of augmented space (introduced in EA) and harnesses unlabeled data in target domain to ameliorate the transfer of information from source to target . This semisupervised approach to domain adaptation is extremely simple to implement, and can be applied as a pre-processing step to any supervised learner. Experimental results on sequential labeling tasks demonstrate the efficacy of the proposed method.