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In this paper, we propose three different methods for automatic evaluation of the machine translation (MT) quality. Two of the metrics are trainable on directassessment scores and two of them use dependency structures. The trainable metric AutoDA, which uses deep-syntactic features, achieved better correlation with humans compared e.g. to the chrF3 metric.

CUNI Experiments for WMT17 Metrics Task