Adaptive Context-Dependent Machine Translation for Heterogeneous Text

虽然自动机器翻译技术无疑对广泛的用户有用，但它们经常为许多类型的输入产生不连贯的输出，例如，医学、文学甚至对话文本。该项目将开发新的自适应机器翻译系统，以处理许多领域和文本风格，包括不同的混合域输入。它将开发多任务机器学习方法，以训练特定于领域的翻译系统集合，同时利用领域之间的相关性。这种方法将减少当前翻译系统对大数据的要求，并提高各种不同语言对和应用领域的翻译质量。

While automatic machine translation technologies are undoubtedly useful to a wide range of users, they often produce incoherent outputs for many types of input, for example, medical, literature, or even conversational text. This project will develop new adaptive machine translation systems to handle many domains and text styles, including heterogeneous mixed-domain inputs. It will develop multi-task machine learning methods for training collections of domain-specific translation systems while leveraging correlations between domains. This approach will reduce the big data requirements of current translation systems, and improve translation quality across a wide range of different language pairs and application domains.