  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.
