Unsupervised domain adaptation often relies on transforming the instance representation. However, most such approaches are designed for bag-of-words models, and ignore the structured features present in many problems in NLP. We propose a new technique called marginalized structured dropout, which exploits feature structure to obtain a remarkably simple and efficient feature projection. Applied to the task of fine-grained part-of-speech tagging on a dataset of historical Portuguese, marginalized structured dropout yields state-of-the-art accuracy while increasing speed by more than an order-ofmagnitude over previous work.
Yang and Eisenstein REF proposed marginalized structured dropout, which exploited the feature structure to obtain a remarkably simple and efficient feature projection.
Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
ACL
Computer Science