Bilingual Embeddings and Word Alignments for Translation Quality Estimation

Abstract

This paper describes our submission UFAL MULTIVEC to the WMT16 Quality Estimation Shared Task, for EnglishGerman sentence-level post-editing effort prediction and ranking. Our approach exploits the power of bilingual distributed representations, word alignments and also manual post-edits to boost the performance of the baseline QuEst++ set of features.

Our model outperforms the baseline, as well as the winning system in WMT15, Referential Translation Machines (RTM), in both scoring and ranking sub-tasks.

Keywords

• bilingual
• embeddings
• word
• alignments
• translation
• quality
• estimation