Improving Evaluation of Document-level Machine Translation Quality Estimation

EACL 2017 Yvette GrahamQingsong MaTimothy BaldwinQun LiuCarla ParraCarolina Scarton

Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable. In this paper, we explore the validity of human annotations currently employed in the evaluation of document-level quality estimation for machine translation (MT)... (read more)

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