Abstract
In this report paper we propose three different methods for automatic evaluation of the machine translation (MT) quality. Two of the metrics are trainable on direct-assessment 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.