Regressive Ensemble for Machine Translation Quality Evaluation

Michal Štefánik; Vít Novotný; Petr Sojka

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arXiv · 2109.07242

Regressive Ensemble for Machine Translation Quality Evaluation

Michal Štefánik·Vít Novotný·Petr Sojka

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Abstract

This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics. We evaluate the ensemble using a correlation to expert-based MQM scores of the WMT 2021 Metrics workshop. In both monolingual and zero-shot cross-lingual settings, we show a significant performance improvement over single metrics. In the cross-lingual settings, we also demonstrate that an ensemble approach is well-applicable to unseen languages. Furthermore, we identify a strong reference-free baseline that consistently outperforms the commonly-used BLEU and METEOR measures and significantly improves our ensemble's performance.

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BibTeX RIS

Michal Štefánik, Vít Novotný, Petr Sojka. 2021-09-15. Regressive Ensemble for Machine Translation Quality Evaluation. https://arxiv.org/abs/2109.07242

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