COMET: A Neural Framework for MT Evaluation

We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-theart levels of correlation with human judgements.Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality.To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metrics.Our models achieve new state-ofthe-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.