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We frame the task of machine translation evaluation as one of scoring machinetranslation output with a sequence-to-sequence paraphraser, conditioned on ahuman reference. We propose training the paraphraser as a multilingual NMTsystem, treating paraphrasing as a zero-shot translation task (e.g., Czech toCzech). This results in the paraphraser's output mode being centered around acopy of the input sequence, which represents the best case scenario where theMT system output matches a human reference. Our method is simple and intuitive,and does not require human judgements for training. Our single model (trainedin 39 languages) outperforms or statistically ties with all prior metrics onthe WMT 2019 segment-level shared metrics task in all languages (excludingGujarati where the model had no training data). We also explore using our modelfor the task of quality estimation as a metric--conditioning on the sourceinstead of the reference--and find that it significantly outperforms everysubmission to the WMT 2019 shared task on quality estimation in every languagepair.

Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing