chrF
Related Papers
chrF: character n-gram F-score for automatic MT evaluation
We propose the use of character n-gram F-score for automatic evaluation of ma- chine translation output. Character n- grams have already been used as a part of more complex metrics, …...
Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust
AbstractThis paper presents the results of the WMT22 Metrics Shared Task. Participants submitting automatic MT evaluation metrics were asked to score the outputs of the translation systems competing in the …...
Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages
AbstractThis paper presents the work of team PICT-NLP for the shared task on unsupervised and very low-resource supervised machine translation, organized by the Workshop on Machine Translation, a workshop in …...
BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation
AbstractAlthough neural-based machine translation evaluation metrics, such as COMET or BLEURT, have achieved strong correlations with human judgements, they are sometimes unreliable in detecting certain phenomena that can be considered …...
Extrinsic Evaluation of Machine Translation Metrics
AbstractAutomatic machine translation (MT) metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets (system-level evaluation). However, it is unclear if automatic metrics …...
Quality Analysis of Multilingual Neural Machine Translation Systems and Reference Test Translations for the English-Romanian language pair in the Medical Domain
AbstractMultilingual Neural Machine Translation (MNMT) models allow to translate across multiple languages based on only one system. We study the quality of a domain-adapted MNMT model in the medical domain …...