EAGER: Building Language Technologies by Machine Reading Grammars

EAGER：通过机器阅读语法构建语言技术

• 批准号: 2327143
• 负责人: Antonios Anastasopoulos
• 金额: $ 9.93万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327143&HistoricalAwards=false
• 关键词: EAGER; Building; Language; Technologies; Machine

Recent years have seen incredible advances in natural language processing (NLP) technologies, which now make it possible to perform numerous tasks through, with, or on language data. However, this progress has been limited to the handful of languages for which abundant data are available, because the neural models that facilitate the recent improvements are particularly data hungry. This work suggests that we should move away from the current data-inefficient learning paradigm, and instead attempt to also model languages by relying on the human mode of describing them: the grammar of each language. Put simply, we will aim to incorporate the grammars of languages, as written by linguists and treated as symbolic knowledge bases, in the process of training neural language models. Specifically, this work will focus on the first step towards this goal, namely extracting the necessary information from grammar descriptions and other linguistic documents. We will explore several alternative modeling approaches, first by relying on retrieval-based models. We will additionally attack the problem through a machine-reading and question-answering framework. Ultimately, the success of these methods will enable the creation of linguistically-informed models, which will in turn facilitate the creation of technologies especially for under-served language communities.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.

近年来，自然语言处理（NLP）技术的进步令人难以置信，这使得可以通过，使用语言数据执行许多任务成为可能。但是，这种进展仅限于可获得丰富数据的少数语言，因为促进最近改进的神经模型特别饥饿。这项工作表明，我们应该远离当前的数据智能学习范式，而是尝试通过依靠人类描述它们的模式来建模语言：每种语言的语法。简而言之，在培训神经语言模型的过程中，我们将旨在将语言语法（由语言学家撰写并被视为象征性知识基础）结合在一起。具体而言，这项工作将集中在朝着这一目标的第一步上，即从语法描述和其他语言文档中提取必要的信息。我们将首先依靠基于检索的模型来探索几种替代建模方法。我们还将通过机器阅读和提问框架来攻击问题。最终，这些方法的成功将能够创建语言知名的模型，这反过来又可以促进技术的创建，尤其是针对服务不足的语言社区的创建。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查审查标准来通过评估来获得支持的。