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Climbing the tower of babel: Unsupervised multilingual learning

Author(s)
Snyder, Benjamin; Barzilay, Regina
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Abstract
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
2010-06
URI
http://hdl.handle.net/1721.1/61698
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
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

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