Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models

• URL: http://arxiv.org/abs/2302.03491v1
• Date: Tue, 7 Feb 2023 14:35:35 GMT
• Title: Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models
• Authors: Amirkeivan Mohtashami, Mauro Verzetti, Paul K. Rubenstein
• Abstract summary: We show how knowledge can be distilled from Large Language Models (LLMs) to improve upon learned metrics without requiring human annotators. We show that the performance of a BLEURT-like model on lower resource languages can be improved in this way.
• Score: 4.168157981135698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learned metrics such as BLEURT have in recent years become widely employed to evaluate the quality of machine translation systems. Training such metrics requires data which can be expensive and difficult to acquire, particularly for lower-resource languages. We show how knowledge can be distilled from Large Language Models (LLMs) to improve upon such learned metrics without requiring human annotators, by creating synthetic datasets which can be mixed into existing datasets, requiring only a corpus of text in the target language. We show that the performance of a BLEURT-like model on lower resource languages can be improved in this way.

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