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Natural Language Processing

Tharindu Ranasinghe, University of Wolverhampton

TransQuest: Translation Quality Estimation with Cross-lingual Transformers

1 October 2020


High-accuracy translation quality estimation (QE) that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows. Even though there are many systems that can do QE, majority of these methods work only on the language pair they are trained on and need retraining for new language pairs which can be usually computationally expensive and difficult especially for low-resource language pairs. As a solution, in this presentation, we introduce TransQuest – a simple QE framework based on cross-lingual transformers. TransQuest outperforms the current state of the art quality estimation methods like DeepQuest and OpenKiwi. This is also the winning solution in recently concluded WMT 2020 sentence-level Direct Assessment shared task, winning all the language pairs with the multilingual track too.