论文标题
WMT20中无监督和非常低的资源翻译任务的CUNI系统
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20
论文作者
论文摘要
本文介绍了对WMT20任务的CUNI系统的描述，内容涉及德语和上索尔比亚人之间的无监督且非常低的负责监督机器翻译。我们试验了有关综合数据的培训，并在相关语言对上进行了预培训。在完全无监督的场景中，我们分别达到了25.5和23.7 BLEU，分别翻译成上层Sorbian。我们的低资源系统依赖于从德国与奇迹并行数据中转移学习，并获得了57.4 BLEU和56.1 BLEU，这是仅在可用的小型德国 - Upper Sorbian Parallel Corpus上训练的基线上的10个BLEU积分。
This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian. We experimented with training on synthetic data and pre-training on a related language pair. In the fully unsupervised scenario, we achieved 25.5 and 23.7 BLEU translating from and into Upper Sorbian, respectively. Our low-resource systems relied on transfer learning from German-Czech parallel data and achieved 57.4 BLEU and 56.1 BLEU, which is an improvement of 10 BLEU points over the baseline trained only on the available small German-Upper Sorbian parallel corpus.
