Predicting Post-Editor Profiles from the Translation Process
Karan Singla, David Orrego-Carmona, Ashleigh R. Gonzales, Michael H. Carl, Srinivas Bangalore · CBS Research Portal (Copenhagen Business School) · 2014
The purpose of the current investigation is to predict post-editor profiles based on user be- haviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT Translation Process Research Database (TPR-DB). The analysis has two main research goals: We create n-gram models based on user activity and part-of-speech sequences to automatically cluster post-editors, and we use discriminative classifier models to character- ize post-editors based on a diverse range of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities.