Collaborative Research: Language Documentation with an Artificial Intelligence (AI) Helper

记录语言，特别是濒危语言，对于保护人类的知识和文化遗产，以及促进对人类语言的理解至关重要。传统的文档方法产生了诸如语法、字典和注释文本等宝贵的材料，但是需要更多的时间来跟上当前语言灭绝的速度。对这一危机最具建设性的回应是补充文献工作，现在收集尽可能多的语言的数据，并使它们易于获取和解释，以便语言学家和语言社区的成员以后可以对它们进行研究。数字技术使得获取一种濒危语言长达数小时的录音以及翻译成为可能。该项目推进了在子词、词和子句级别分析录音的技术，使它们能够用于各种记录目的。该项目通过三种方式使数字记录中的信息更易于解释，从而便于进一步的语言分析。首先，该团队正在设计计算方法，以自动获得基本的语音理解，并产生语言的语音表示，即使它们没有既定的书写系统。其次，该团队正在开发自动分析语言中高度复杂的单词内部结构的方法。第三，该团队利用更广泛使用的语言知识来分析相关的濒危语言。由此产生的工具，即人工智能助手工具箱，将与语言学家和语言社区在语言文档过程中广泛使用的软件一起打包。所有工具都可以通过基于web的界面访问，源代码将通过GitHub公开提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Documentation of languages, especially endangered languages, is crucial for conserving humanity’s knowledge and cultural heritage, as well as for advancing an understanding of human language. Traditional documentation methods produce invaluable materials such as grammars, dictionaries, and annotated texts, but require more time than can be afforded to keep up with current language extinction rates. The most constructive response to this crisis is to complement documentation efforts by collecting data for as many languages as possible now and to make them accessible and interpretable so that they can be studied later by both linguists and members of the language communities. Digital technologies make it practical to obtain many hours of recordings in an endangered language along with translations. This project advances technologies for analyzing the recordings at the sub-word, word, and clause level so that they become accessible for a wide variety of documentary purposes.The project makes the information in digital recordings more interpretable for further linguistic analysis in three ways. First, the team is devising computational methods to automatically derive a basic phonological understanding and produce phonetic representations for languages, even if they do not have an established writing system. Second, the team is developing methods to automatically analyze the internal structure of words in languages where this structure is highly complex. Third, the team uses knowledge of more widely spoken languages to analyze related endangered languages. The resulting tool, the AI-helper toolbox, will be packaged with software that is currently widely in use by linguists and language communities in the language documentation process. All tools will be accessible through a web-based interface and the source code will be publicly available through GitHub.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.