Predicting Post-Editor Profiles from the Translation Process

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Predicting Post-Editor Profiles from the Translation Process

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Titel: Predicting Post-Editor Profiles from the Translation Process
Forfatter: Singla, Karan; Orrego-Carmona, David; Gonzales, Ashleigh Rhea; Carl, Michael; Bangalore, Srinivas
Resume: 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.
URI: http://hdl.handle.net/10398/9071
Dato: 2014-12-17
Note: Paper presented at The 11th Conference of the Association for Machine Translation in the Americas 2014, okt. 22-26. Vancouver, Canada

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