论文标题
Unberbel参与WMT20指标共享任务
Unbabel's Participation in the WMT20 Metrics Shared Task
论文作者
论文摘要
我们介绍了不受欢迎的团队对WMT 2020指标共享任务的贡献。我们打算参加所有语言对的细分级，文档级和系统级轨道，以及“ QE为度量”轨道。因此，我们参考了上一年的测试集，在这些轨道中说明了模型的结果。我们的提交基于最近提出的彗星框架：我们训练多个估算器模型，以回归不同的人类生成的质量得分，并以从直接评估获得的相对等级训练的新型排名模型。我们还提出了一种简单的技术，用于将细分级预测转换为文档级别得分。总体而言，我们的系统在先前的测试集上为所有语言对取得了强劲的成果，并且在许多情况下，我们的系统设定了新的最先进。
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.
