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arXiv · 2009.09025

COMET: A Neural Framework for MT Evaluation

Ricardo Rei·Craig Stewart·Ana C Farinha·Alon Lavie
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Abstract

We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art 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-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.

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Ricardo Rei, Craig Stewart, Ana C Farinha, Alon Lavie. 2020-09-18. COMET: A Neural Framework for MT Evaluation. https://arxiv.org/abs/2009.09025

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