TransQuest: Translation Quality Estimation with Cross-lingual Transformers
Tharindu Ranasinghe, Constantin Orǎsan, Ruslan Mitkov · 2020
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.Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT.In addition, the framework proves very useful in transfer learning settings, especially when dealing with low-resourced languages, allowing us to obtain very competitive results.