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

Understand

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.