Regressive Ensemble for Machine Translation Quality Evaluation
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Proceedings of EMNLP 2021 Sixth Conference on Machine Translation (WMT 21) |
| MU Faculty or unit | |
| Citation | |
| Web | |
| 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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