Regressive Ensemble for Machine Translation Quality Evaluation

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Authors

ŠTEFÁNIK Michal NOVOTNÝ Vít SOJKA Petr

Year of publication 2021
Type Article in Proceedings
Conference Proceedings of EMNLP 2021 Sixth Conference on Machine Translation (WMT 21)
MU Faculty or unit

Faculty of Informatics

Citation
Web preprint
Keywords machine translation; translation quality metrics; regressive ensemble for machine translation quality evaluation
Description 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 improvements over single systems. 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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