Comunicació presentada a la First Conference on Machine Translation (WMT), que es va dur a terme durant el 54th Annual Meeting of the Association for Computational Linguistics, els dies 7 a 12 d'agost de 2016 a Berlín, Alemanya.

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.

This work was partially funded by TUNER (TIN2015-65308-C5-5-R) and MINECO/FEDER, UE. Marina Fomicheva was supported by funding from the FI-DGR grant program of the Generalitat de Catalunya. Iria da Cunha was supported by a Ramon y Cajal contract (RYC-2014-16935). Lucia Specia was supported by the QT21 project (H2020 No. 645452).