CobaltF: A Fluent Metric for MT Evaluation
Marina Fomicheva, Núria Bel, Lucia Specia, Iria da Cunha, Anton Malinovskiy · 2016
The vast majority of Machine Translation (MT) evaluation approaches are based on the idea that the closer the MT output is to a human reference translation, the higher its quality.While translation quality has two important aspects, adequacy and fluency, the existing referencebased metrics are largely focused on the former.In this work we combine our metric UPF-Cobalt, originally presented at the WMT15 Metrics Task, with a number of features intended to capture translation fluency.Experiments show that the integration of fluency-oriented features significantly improves the results, rivalling the best-performing evaluation metrics on the WMT15 data.