Paper: Semi-Supervised Representation Learning for Cross-Lingual Text Classification

ACL ID D13-1153
Title Semi-Supervised Representation Learning for Cross-Lingual Text Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Cross-lingual adaptation aims to learn a pre- diction model in a label-scarce target lan- guage by exploiting labeled data from a label- rich source language. An effective cross- lingual adaptation system can substantially re- duce the manual annotation effort required in many natural language processing tasks. In this paper, we propose a new cross-lingual adaptation approach for document classifica- tion based on learning cross-lingual discrim- inative distributed representations of words. Specifically, we propose to maximize the log- likelihood of the documents from both lan- guage domains under a cross-lingual log- bilinear document model, while minimizing the prediction log-losses of labeled docu- ments. We conduct extensive experiments on cross-lingual sentiment classification tasks o...