Machine Translation (MT) has advanced in recent years to produce better translations for clients' specific domains, and sophisticated tools allow professional translators to obtain translations according to their prior edits. We suggest that MT should be further personalized to the end-user level -the receiver or the author of the text -as done in other applications. As a step in that direction, we propose a method based on a recommender systems approach where the user's preferred translation is predicted based on preferences of similar users. In our experiments, this method outperforms a set of non-personalized methods, suggesting that user preference information can be employed to provide better-suited translations for each user.
Focusing on reader preferences, REF used a collaborative filtering approach from recommender systems, where a user's preferred translation is predicted based on the preferences of similar users.
Personalized Machine Translation: Predicting Translational Preferences
EMNLP
Computer Science