Cost Optimization for Crowdsourcing Translation
Mingkun Gao, Wei Hong Xu, Chris Callison-Burch · 2015
Crowdsourcing makes it possible to create translations at much lower cost than hiring professional translators. However, it is still expensive to obtain the millions of transla-tions that are needed to train statistical ma-chine translation systems. We propose two mechanisms to reduce the cost of crowdsourc-ing while maintaining high translation quality. First, we develop a method to reduce redun-dant translations. We train a linear model to evaluate the translation quality on a sentence-by-sentence basis, and fit a threshold between acceptable and unacceptable translations. Un-like past work, which always paid for a fixed number of translations for each source sen-tence and then chose the best from them, we can stop earlier and pay less when we receive a translation that is good enough. Second, we introduce a method to reduce the pool of translators by quickly identifying bad transla-tors after they have translated only a few sen-tences. This also allows us to rank translators, so that we re-hire only good translators to re-duce cost. 1