Bloodgood, MichaelCallison-Burch, ChrisBuilding machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon’s Mechanical Turk (MTurk) to make MT test sets cheaply. We find that MTurk can be used to make test sets much cheaper than professionally-produced test sets. More importantly, in experiments with multiple MT systems, we find that the MTurk-produced test sets yield essentially the same conclusions regarding system performance as the professionally-produced test sets yield.en-UScomputer sciencestatistical methodsartificial intelligencecomputational linguisticsnatural language processinghuman language technologymachine translationstatistical machine translationmachine translation evaluationcrowdsourcingAmazon Mechanical Turkcost-efficient annotationannotation costsannotation bottlenecktranslation costsUrdu-English translationArticle