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Current Statistical Machine Translation (SMT) is significantly affected by Machine Translation (MT) evaluation metric. Nowadays the emergence of document-level MT research increases the demand for corresponding evaluation metric. This paper proposes two superior yet low-cost quantitative objective methods to enhance traditional MT metric by modeling document-level phenomena from the perspectives of gist consistency and text cohesion. The experimental results show the proposed metrics can obtain better correlation with human judgments than traditional metrics on evaluating document-level translation quality.

Document-Level Machine Translation Evaluation with Gist Consistency and Text Cohesion