Unbabel's Participation in the WMT20 Metrics Shared Task

• URL: http://arxiv.org/abs/2010.15535v1
• Date: Thu, 29 Oct 2020 12:59:44 GMT
• Title: Unbabel's Participation in the WMT20 Metrics Shared Task
• Authors: Ricardo Rei, Craig Stewart, Catarina Farinha, Alon Lavie
• Abstract summary: We present the contribution of the Unbabel team to the WMT 2020 Shared Task on Metrics. We intend to participate on the segment-level, document-level and system-level tracks on all language pairs. We illustrate results of our models in these tracks with reference to test sets from the previous year.
• Score: 8.621669980568822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the contribution of the Unbabel team to the WMT 2020 Shared Task on Metrics. We intend to participate on the segment-level, document-level and system-level tracks on all language pairs, as well as the 'QE as a Metric' track. Accordingly, we illustrate results of our models in these tracks with reference to test sets from the previous year. Our submissions build upon the recently proposed COMET framework: We train several estimator models to regress on different human-generated quality scores and a novel ranking model trained on relative ranks obtained from Direct Assessments. We also propose a simple technique for converting segment-level predictions into a document-level score. Overall, our systems achieve strong results for all language pairs on previous test sets and in many cases set a new state-of-the-art.

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