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

ACL ID P14-1107
Title Are Two Heads Better than One? Crowdsourced Translation via a Two-Step Collaboration of Non-Professional Translators and Editors
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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.