Learning Deep Semantics for Automatic Translation between Human Languages

该项目旨在整合深度语言学和深度学习，以提高翻译质量。现代世界越来越依赖人类语言的自动翻译来处理数十亿文档。目前的翻译系统与复杂的文本作斗争，经常产生误导或不连贯的输出。此外，他们独立翻译句子，忽略了他们的整体文档范围内的上下文。该项目旨在通过开发一种新方法来解决这些问题，该方法使用语义-文本的潜在含义-来驱动翻译，无论是通过深度学习学习的离散结构还是连续表示。这可以提高翻译质量，从而提高最终用户的自动翻译。

This project seeks to integrate deep linguistics and deep learning to improve translation quality. The modern world relies increasingly on automatic translation of human languages to deal with billions of documents. Current translation systems struggle with complex texts and often produce misleading or incoherent outputs. Furthermore, they translate sentences independently and ignore their overall document-wide context. This project seeks to address these issues by developing a new approach using semantics – the underlying meaning of the text – to drive translation, both as discrete structures and continuous representations learned via deep learning. This may improve translation quality, thereby improving automatic translation for end-users.