Unsupervised Domain Adaptation with Feature Embeddings
نویسندگان
چکیده
Representation learning is the dominant technique for unsupervised domain adaptation, but existing approaches often require the specification of “pivot features” that generalize across domains, which are selected by task-specific heuristics. We show that a novel but simple feature embedding approach provides better performance, by exploiting the feature template structure common in NLP problems.
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عنوان ژورنال:
- CoRR
دوره abs/1412.4385 شماره
صفحات -
تاریخ انتشار 2014