Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
Yi Ping Yang, Jacob Eisenstein · 2014
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.