Year: 2014
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Yang, Yi, Eisenstein, Jacob
Institutions: Georgia Institute of Technology, Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1764158405