论文标题
端到端本地化的联合翻译和单位转换
Joint translation and unit conversion for end-to-end localization
论文作者
论文摘要
各种自然语言任务需要处理文本数据，其中包含自然语言和形式语言（例如数学表达式）的混合。在本文中，我们以单元转换为例，并提出了一种数据增强技术，该技术导致模型同时学习翻译和转换任务，以及如何在它们之间充分切换它们以进行端到端的本地化。
A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which leads to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.
