RI: Small: New tools for studying structural and inductive bias in NLP models

RI：小：研究 NLP 模型中的结构和归纳偏差的新工具

• 批准号: 2128145
• 负责人: Daniel Jurafsky
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128145&HistoricalAwards=false
• 关键词: RI; Small; New; tools; studying

Modern natural language processing systems, based on neural networks trained using large amounts of text, are a key part of the infrastructure of the nation and the world. These systems power practical tools like machine translation, web search, or automatic question answering, as well as research tools that help scientists and policy makers. These language processing models have made enormous progress in many ways, yet systems still fail unexpectedly, their successes cannot be explained, and their blind spots lead to biases. This project develops new tools for studying language models: why they work as well as they do, what their limitations are, and what distortions they introduce into language understanding, with the goal of improved systems and helping mitigate negative impacts on society.This project develops and investigates four kinds of new analytic tools for studying the inductive biases of language models - the structural tendencies that determine what they can learn. The structural transfer-learning paradigm involves training language models on artificial languages that can be manipulated, to see which structural aspects improve performance on natural language. The challenge-task paradigm brings humans in the loop to develop new evaluations to study why and how language processing systems fail, such as on aspect of language that change over time. The new theoretical framework of sensitivity models the complexity of language processing tasks by measuring how responsive the classification is to minor changes in the input, demonstrating which tasks or examples are easy or hard. And new tools are introduced to measure how embeddings of words introduce structural distortions - exaggerations or understatements in word relationships - that can cause models to fail. Understanding the limitations of technology and what makes one system better or one task or dataset harder than another is a crucial step toward building better language processing systems.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.

现代自然语言处理系统基于使用大量文本训练的神经网络，是国家和世界基础设施的关键部分。这些系统为机器翻译、网络搜索或自动问答等实用工具以及为科学家和政策制定者提供帮助的研究工具提供支持。这些语言处理模型在许多方面取得了巨大进步，但系统仍然会意外失败，它们的成功无法解释，而且它们的盲点会导致偏差。该项目开发用于研究语言模型的新工具：它们为什么有效、它们的局限性以及它们给语言理解带来的扭曲，目的是改进系统并帮助减轻对社会的负面影响。该项目开发并研究了四种新的分析工具，用于研究语言模型的归纳偏差 - 决定它们可以学到什么的结构倾向。结构迁移学习范式涉及在可操作的人工语言上训练语言模型，以了解哪些结构方面可以提高自然语言的性能。挑战任务范式让人类参与开发新的评估，以研究语言处理系统失败的原因和方式，例如随时间变化的语言方面。新的敏感性理论框架通过测量分类对输入微小变化的响应程度来模拟语言处理任务的复杂性，展示哪些任务或示例是简单或困难的。还引入了新工具来衡量单词嵌入如何引入结构扭曲（单词关系中的夸大或轻描淡写），这可能导致模型失败。了解技术的局限性以及是什么使一个系统更好或一个任务或数据集比另一个系统更难，是构建更好的语言处理系统的关键一步。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。