Are Two Heads Better than One? Crowdsourced Translation via a Two-Step Collaboration of Non-Professional Translators and Editors
Rui Yan, Mingkun Gao, Ellie Pavlick, Chris Callison-Burch · 2014
Crowdsourcing is a viable mechanism for creating training data for machine translation.It provides a low cost, fast turnaround way of processing large volumes of data.However, when compared to professional translation, naive collection of translations from non-professionals yields low-quality results.Careful quality control 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 graphbased ranking models that automatically select the best output from multiple redundant versions of translations and edits, and improves translation quality closer to professionals.