Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation

Abstract

We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.

11 pages, 14 figures; appeared in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, July 2010

References in corpus (1)

• Using Mechanical Turk to Build Machine Translation Evaluation Sets

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