Predicting translation performance with referential translation machines

Abstract

Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the 3rd system results for German to English sentence-level prediction of translation quality and the 2nd system results according to root mean squared error. In addition to the new features about substring distances, punctuation tokens, character n-grams, and alignment crossings, and additional learning models, we average prediction scores from different models using weights based on their training performance for improved results.