BlonD: An Automatic Evaluation Metric for Document-level MachineTranslation

• URL: http://arxiv.org/abs/2103.11878v1
• Date: Mon, 22 Mar 2021 14:14:58 GMT
• Title: BlonD: An Automatic Evaluation Metric for Document-level MachineTranslation
• Authors: Yuchen Jiang, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang and Ming Zhou
• Abstract summary: We propose an automatic metric BlonD for document-level machine translation evaluation. BlonD takes discourse coherence into consideration by calculating the recall and distance of check-pointing phrases and tags.
• Score: 47.691277066346665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard automatic metrics (such as BLEU) are problematic for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones nor can they identify the specific discourse phenomena that caused the translation errors. To address these problems, we propose an automatic metric BlonD for document-level machine translation evaluation. BlonD takes discourse coherence into consideration by calculating the recall and distance of check-pointing phrases and tags, and further provides comprehensive evaluation scores by combining with n-gram. Extensive comparisons between BlonD and existing evaluation metrics are conducted to illustrate their critical distinctions. Experimental results show that BlonD has a much higher document-level sensitivity with respect to previous metrics. The human evaluation also reveals high Pearson R correlation values between BlonD scores and manual quality judgments.

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