论文标题
阿里巴巴 - 翻译中国对WMT 2022指标共享任务的提交
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
论文作者
论文摘要
在本报告中，我们将提交给WMT 2022指标共享任务。我们基于Unite（统一翻译评估）的核心思想构建系统，该核心构建了一个单一模型，该统一将仅源，仅参考和源参考的评估方案统一。具体而言，在模型预训练阶段，我们首先将伪标记的数据示例应用于连续预训练Unite。值得注意的是，为了减少培训和微调之间的差距，我们使用数据裁切和基于排名的分数归一化策略。在微调阶段，我们使用了过去几年WMT竞赛的直接评估（DA）和多维质量指标（MQM）数据。特别是，我们从具有不同预训练的语言模型骨架的模型中收集结果，并使用不同的结合策略来涉及翻译方向。
In this report, we present our submission to the WMT 2022 Metrics Shared Task. We build our system based on the core idea of UNITE (Unified Translation Evaluation), which unifies source-only, reference-only, and source-reference-combined evaluation scenarios into one single model. Specifically, during the model pre-training phase, we first apply the pseudo-labeled data examples to continuously pre-train UNITE. Notably, to reduce the gap between pre-training and fine-tuning, we use data cropping and a ranking-based score normalization strategy. During the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions. Specially, we collect the results from models with different pre-trained language model backbones, and use different ensembling strategies for involved translation directions.
