论文标题
分层多语言机器翻译的框架
A Framework for Hierarchical Multilingual Machine Translation
论文作者
论文摘要
多语言的机器翻译最近在Vogue中流行，鉴于通过转移学习来改善低资源语言的机器翻译性能。然而，证明现有多语言机器翻译策略成功的经验检查仅限于特定语言组的实验。在本文中，我们提出了一个层次结构框架，用于构建多语言机器翻译策略，该策略利用了类型的语言家族树来启用类似语言之间的传递，同时避免了融合彼此之间太不同的语言产生的负面影响。在具有41种语言的数据集上进行的详尽实验证明了该框架的有效性，尤其是在通过使用与类型学相关的家庭使用更丰富的资源可用的类型家庭来改善低资源语言的性能。
Multilingual machine translation has recently been in vogue given its potential for improving machine translation performance for low-resource languages via transfer learning. Empirical examinations demonstrating the success of existing multilingual machine translation strategies, however, are limited to experiments in specific language groups. In this paper, we present a hierarchical framework for building multilingual machine translation strategies that takes advantage of a typological language family tree for enabling transfer among similar languages while avoiding the negative effects that result from incorporating languages that are too different to each other. Exhaustive experimentation on a dataset with 41 languages demonstrates the validity of the proposed framework, especially when it comes to improving the performance of low-resource languages via the use of typologically related families for which richer sets of resources are available.
