BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation

Yuchen Eleanor Jiang; Tianyu Liu; Shuming Ma; Dongdong Zhang; Jian Yang; Haoyang Huang; Rico Sennrich; Ryan Cotterell; Mrinmaya Sachan; Ming Zhou

arXiv · 2103.11878

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation

Yuchen Eleanor Jiang·Tianyu Liu·Shuming Ma·Dongdong Zhang·Jian Yang·Haoyang Huang·Rico Sennrich·Ryan Cotterell·Mrinmaya Sachan·Ming Zhou

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Abstract

Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations. This paper introduces a novel automatic metric BlonDe to widen the scope of automatic MT evaluation from sentence to document level. BlonDe takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. We conduct extensive comparisons on a newly constructed dataset BWB. The experimental results show that BlonDe possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances. In a large-scale human study, BlonDe also achieves significantly higher Pearson's r correlation with human judgments compared to previous metrics.

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Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou. 2021-03-22. BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation. https://arxiv.org/abs/2103.11878

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