This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tracks including Large Track and two Small Tracks where the former one is unconstrained and the latter two are fully constrained. Our model submissions to the shared task were initialized with DeltaLM\footnote{\url{https://aka.ms/deltalm}}, a generic pre-trained multilingual encoder-decoder model, and fine-tuned correspondingly with the vast collected parallel data and allowed data sources according to track settings, together with applying progressive learning and iterative back-translation approaches to further improve the performance. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
翻译:本报告介绍了微软用于WMT21大型多语种机器翻译共同任务的机器翻译系统,我们参与了所有三个评价轨道,包括大轨和两条小轨,前者不受限制,后两条完全受限制。我们对共同任务的示范呈件与DeltaLM\ footnote_url{https://akas/ms/deltalm}(这是通用的预先培训的多语种编码器-解码器模式)初始化,并与大量收集的平行数据和允许的数据源根据跟踪环境进行微调,同时采用渐进学习和迭代回翻译方法进一步改进业绩。我们的最后呈件在自动评价指标方面排在三条轨道上排第一。