Predicting post-editor profiles from the translation process
Karan Singla, David Orrego-Carmona, Ashleigh Rhea Gonzales, Michael Carl, Srinivas Bangalore
Abstract
The purpose of the current investigation is to predict post-editor profiles based on user behaviour 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 characterize 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.- Anthology ID:
- 2014.amta-workshop.6
- Volume:
- Workshop on interactive and adaptive machine translation
- Month:
- October 22
- Year:
- 2014
- Address:
- Vancouver, Canada
- Editors:
- Francisco Casacuberta, Marcello Federico, Philipp Koehn
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 51–60
- Language:
- URL:
- https://aclanthology.org/2014.amta-workshop.6
- DOI:
- Cite (ACL):
- Karan Singla, David Orrego-Carmona, Ashleigh Rhea Gonzales, Michael Carl, and Srinivas Bangalore. 2014. Predicting post-editor profiles from the translation process. In Workshop on interactive and adaptive machine translation, pages 51–60, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Predicting post-editor profiles from the translation process (Singla et al., AMTA 2014)
- PDF:
- https://preview.aclanthology.org/landing_page/2014.amta-workshop.6.pdf