Paper: Is Machine Translation Ripe for Cross-Lingual Sentiment Classification?

ACL ID P11-2075
Title Is Machine Translation Ripe for Cross-Lingual Sentiment Classification?
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Recent advances in Machine Translation (MT) have brought forth a new paradigm for build- ing NLP applications in low-resource scenar- ios. To build a sentiment classifier for a language with no labeled resources, one can translate labeled data from another language, then train a classifier on the translated text. This can be viewed as a domain adaptation problem, where labeled translations and test data have some mismatch. Various prior work have achieved positive results using this ap- proach. In this opinion piece, we take a step back and make some general statements about cross- lingual adaptation problems. First, we claim that domain mismatch is not caused by MT errors, and accuracy degradation will occur even in the case of perfect MT. Second, we ar- gue that the cross-lingual adaptat...