论文标题
COVID-19 MLIA机器翻译任务的发现
Findings of the Covid-19 MLIA Machine Translation Task
论文作者
论文摘要
这项工作介绍了COVID-19 MLIA @ estiative的机器翻译（MT）任务的结果，这是社区的努力，以改善关注当前Covid-19-19的MT系统的产生。九个团队参加了这项活动，该活动分为两轮，涉及七个不同的语言对。考虑了两种不同的方案：一种仅允许提供的数据，而第二个则允许使用外部资源。总体而言，最佳方法是基于多语言模型和转移学习，重点是将清洁过程应用于培训数据的重要性。
This work presents the results of the machine translation (MT) task from the Covid-19 MLIA @ Eval initiative, a community effort to improve the generation of MT systems focused on the current Covid-19 crisis. Nine teams took part in this event, which was divided in two rounds and involved seven different language pairs. Two different scenarios were considered: one in which only the provided data was allowed, and a second one in which the use of external resources was allowed. Overall, best approaches were based on multilingual models and transfer learning, with an emphasis on the importance of applying a cleaning process to the training data.
