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As research on machine translation moves to translating text beyond thesentence level, it remains unclear how effective automatic evaluation metricsare at scoring longer translations. In this work, we first propose a method forcreating paragraph-level data for training and meta-evaluating metrics fromexisting sentence-level data. Then, we use these new datasets to benchmarkexisting sentence-level metrics as well as train learned metrics at theparagraph level. Interestingly, our experimental results demonstrate that usingsentence-level metrics to score entire paragraphs is equally as effective asusing a metric designed to work at the paragraph level. We speculate thisresult can be attributed to properties of the task of reference-basedevaluation as well as limitations of our datasets with respect to capturing alltypes of phenomena that occur in paragraph-level translations.

Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level