TransQuest: Translation Quality Estimation with Cross-lingual Transformers

Understand

Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures.

• However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs.
• This process can prove difficult from a technical point of view and is usually computationally expensive.
• In this paper we propose a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures.