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Invited Applications Paper Climbing the Tower of Babel: Unsupervised Multilingual Learning

2020-02-27 | |  75 |   36 |   0

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.

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