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. ...
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.
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