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Assessing Reference-Free Peer Evaluation for Machine Translation

تقييم تقييم الأقران الخالي من المرجعية للترجمة الآلية

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-free metric. We experiment with various modifications to this model, and demonstrate that by scaling it up we can match the performance of BLEU. We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities.



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