EAGER: Machine Translation for Language Preservation

在过去的50年里，计算语言学的研究只涉及到世界上1%的语言。 在100年内，90%的物种将灭绝或接近灭绝。 计算语言学能提供什么来支持记录和分析世界濒危语言的紧迫任务？ 双语平行文本是文献语言学收集的主要工件，也是统计翻译模型的主要对象，基于这一观察，本项目探索使用机器翻译来加速全球语言文献工作。 具体来说，它开发了新的方法来同时对任何数量的相关语言进行建模，从所有语言中汇集信息，以对每种语言进行更强的推断。 为了利用语言的关系，它探讨的方法，同时模拟语音，形态，词汇和句法现象。 这些技术将在巴布亚新几内亚的东部高地进行实地测试，旨在使那些没有受过专门语言训练的濒危语言使用者能够创建大量翻译的口头文学作品，为他们的语言提供真实和可解释的记录，为当代和后代的学者，教师，和学习者。 此外，他们这样做的成本远远低于支持训练有素的语言学家和人种学家努力创建此类收藏所需的成本。

In the last 50 years, computational linguistics research has touched barely 1% of the world's languages. In 100 years, 90% of them will be extinct or nearly so. What can computational linguistics offer to support the urgent task of documenting and analyzing the world's endangered languages? Based on the observation that bilingual parallel text is both the primary artifact collected in documentary linguistics as well as the primary object of statistical translation models, this project explores the use of machine translation to accelerate the global language documentation effort. Specifically, it develops novel ways to model any number of related languages simultaneously, pooling information from all the languages to make stronger inferences about each. In order to exploit language relationships, it explores methods that simultaneously model phonological, morphological, lexical, and syntactic phenomena. In addition, it develops algorithms to standardize highly variable transcription practices.These technologies, which will be field-tested in the Eastern Highlands of Papua New Guinea, are designed to enable speakers of endangered languages who have no specialized linguistic training to create large collections of translated oral literature, providing an authentic and interpretable record of their language, serving current and future generations of scholars, teachers, and learners. They will do so, moreover, at much less cost than is needed to support the efforts to trained linguists and ethnographers to create such collections.