Climbing the tower of babel: Unsupervised multilingual learning
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For centuries, scholars have explored the deep
links among human languages. In this paper,
we present a class of probabilistic models
that use these links as a form of naturally
occurring supervision. These models allow
us to substantially improve performance for
core text processing tasks, such as morphological
segmentation, part-of-speech tagging,
and syntactic parsing. Besides these traditional
NLP tasks, we also present a multilingual
model for the computational decipherment
of lost languages.
Date issued
2010-06Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Proceedings of the 27th International Conference on Machine Learning (ICML-10)
Publisher
Omnipress
Citation
Snyder, Benjamin, and Regina Barzilay. “Climbing the Tower of Babel: Unsupervised Multilingual Learning.” Proceedings of the 27th International Conference on Machine Learning (ICML-10). Haifa, Israel:29-36.
Version: Author's final manuscript
ISBN
978-1-60558-907-7