  This paper explores the use of Deep Learning methods for automatic estimation
of quality of human translations. Automatic estimation can provide useful
feedback for translation teaching, examination and quality control.
Conventional methods for solving this task rely on manually engineered features
and external knowledge. This paper presents an end-to-end neural model without
feature engineering, incorporating a cross attention mechanism to detect which
parts in sentence pairs are most relevant for assessing quality. Another
contribution concerns of prediction of fine-grained scores for measuring
different aspects of translation quality. Empirical results on a large human
annotated dataset show that the neural model outperforms feature-based methods
significantly. The dataset and the tools are available.
