论文标题
一起成长：用n-最佳多检查点的机器翻译对人类语言学习建模
Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation
论文作者
论文摘要
我们描述了我们对2020年Duolingo共享的任务的同时翻译和语言教育解释（Mayhew等，2020）的提交。我们将MT模型视为不同级别的人类学习者。因此，我们采用了来自同一模型的多检查点的集合来生成具有各种流利程度的翻译序列。从每个检查点来说，对于我们的最佳模型，我们采样了梁宽度= 100的n-pest序列（n = 10）。我们在官方英语对葡萄牙共享任务测试数据上的6个检查点模型集合中实现了37.57宏F1，表现优于21.30宏F1的基线亚马逊翻译系统，并最终证明了我们直观方法的实用性。
We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE) (Mayhew et al., 2020). We view MT models at various training stages (i.e., checkpoints) as human learners at different levels. Hence, we employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. From each checkpoint, for our best model, we sample n-Best sequences (n=10) with a beam width =100. We achieve 37.57 macro F1 with a 6 checkpoint model ensemble on the official English to Portuguese shared task test data, outperforming a baseline Amazon translation system of 21.30 macro F1 and ultimately demonstrating the utility of our intuitive method.
