EAGER: Machine Translation for Language Preservation

在过去的50年里，计算语言学的研究只涉及了世界上1%的语言。在100年内，它们中的90%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？基于双语平行文本既是文献语言学中收集的主要工件，也是统计翻译模型的主要对象的观察，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，它开发了一种新颖的方法，可以同时对任意数量的相关语言进行建模，汇集所有语言的信息，从而对每种语言做出更有力的推断。为了开发语言关系，它探索了同时模拟语音、形态、词汇和句法现象的方法。此外，它还开发了算法来标准化高度可变的转录实践。这些技术将在巴布亚新几内亚东部高地进行实地测试，旨在使没有接受过专门语言培训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、教师和学习者提供服务。此外，他们这样做的成本要比支持训练有素的语言学家和民族志学家创建这样的收藏所需的成本低得多。

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.