Are Two Heads Better than One? Crowdsourced Translation via a Two-Step Collaboration of Non-Professional Translators and Editors

Crowdsourcing is a viable mechanism for creating training data for machine trans-lation. It provides a low cost, fast turn-around way of processing large volumes of data. However, when compared to pro-fessional translation, naive collection of translations from non-professionals yields low-quality results. Careful quality con-trol is necessary for crowdsourcing to work well. In this paper, we examine the challenges of a two-step collaboration process with translation and post-editing by non-professionals. We develop graph-based ranking models that automatically select the best output from multiple redun-dant versions of translations and edits, and improves translation quality closer to pro-fessionals.