EAGER: Building Idiomaticity into Natural Language Processing

EAGER：将惯用性融入自然语言处理

• 批准号: 2230817
• 负责人: Suma Bhat
• 金额: $ 15万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230817&HistoricalAwards=false
• 关键词: EAGER; Building; Idiomaticity; into; Natural

Idiomatic expressions are an essential component of everyday language use and the hallmark of native language ability. Consider the phrase throw away; proficient speakers can effortlessly understand that the phrase takes a figurative meaning in “Britain threw away all the achievements of the last decade.” and a literal sense in “He threw away his cigarette and buried his head in his arms.” This EArly Grant for Exploratory Research (EAGER) will build a high-quality dataset for computers to understand the differences between figurative and literal senses of these expressions in general English text. The main novelty of this project will be in collecting a large class of idiomatic expressions and sentences containing them to let computers learn the inherent variability between a variety of idiomatic phrases. Collecting many sentences with phrases that have a figurative and literal meaning will permit computers better understand the nuances with which these expressions are used in everyday conversations and writing. Beyond understanding them, the collected. examples will help computers use these expressions like native speakers do when automatically writing text and even suggest appropriate expressions in specific contexts.This EAGER project is essentially interdisciplinary spanning the areas of linguistics and computation and will investigate novel paradigms for natural language processing that are idiomaticity-aware. As such, it will have two research aims: (1) creating a high-quality dataset of phrasal verbs annotated with their context-specific senses and their literal/figurative equivalent forms, and (2) testing the performance of state-of-the-art idiomaticity-aware algorithms. Because idiomatic expressions vary widely in form and structure, the focus on phrasal verbs (also known as verb-particle constructions) in the context of the exploratory project will permit studying a very frequent class of idiomatic expressions that are syntactically different from those in currently available datasets. The primary risk of this project stems from its exploratory nature of creating large corpora with sufficient coverage for language model training. Given their prevalence in natural language, the dataset of phrasal verbs in English will supplement available datasets on idiomatic expressions in terms of their variety. Moreover, their figurative and literal ambiguity in context (apart from their polysemy) will permit a diverse look at the phenomenon of non-compositionality that characterizes idiomatic expressions. Thus, the dataset will serve as a training and test bed for algorithms that detect, interpret, and generate a broad class of idiomatic expressions. This effort will lead to new natural language processing algorithms for accurate interpretation and generation of idiomatic expressions towards a more human-like language processing ability in machines.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.

习惯用语是日常语言使用的重要组成部分，也是母语能力的标志。想想throw away这个短语；精通英语的人可以毫不费力地理解这个短语在“英国扔掉了过去十年的所有成就”中有比喻意义，在“他扔掉了香烟，把头埋在胳膊里”中有字面意义。这项早期探索性研究拨款（EAGER）将为计算机建立一个高质量的数据集，以理解这些表达在一般英语文本中比喻意义和字面意义之间的差异。这个项目的主要新颖之处在于收集大量的习语表达和包含它们的句子，让计算机学习各种习语短语之间的内在可变性。收集许多具有比喻意义和字面意义的短语，将使计算机更好地理解这些短语在日常对话和写作中使用的细微差别。除了理解他们，收集。示例将帮助计算机像母语人士在自动书写文本时那样使用这些表达，甚至在特定语境中建议适当的表达。这个EAGER项目本质上是跨语言学和计算领域的跨学科项目，将研究具有习惯感知的自然语言处理的新范式。因此，它将有两个研究目标：(1)创建一个高质量的动词短语数据集，其中标注了上下文特定的意义和它们的字面/比喻等效形式；(2)测试最先进的习语感知算法的性能。由于习语表达在形式和结构上有很大的不同，因此在探索性项目的背景下，关注短语动词（也称为动词-粒子结构）将允许研究非常频繁的习语表达类别，这些习语表达在语法上与当前可用数据集中的习语表达不同。这个项目的主要风险源于它的探索性，即创建具有足够覆盖语言模型训练的大型语料库。鉴于它们在自然语言中的普遍存在，英语动词短语数据集将在其多样性方面补充现有的习语表达数据集。此外，它们在上下文中的比喻和字面上的歧义（除了它们的一词多义之外）将允许人们从不同的角度看待习语表达的非组合性现象。因此，该数据集将作为算法的训练和测试平台，用于检测、解释和生成广泛类别的惯用表达式。这一努力将导致新的自然语言处理算法，用于准确解释和生成习惯用语，从而使机器具有更像人类的语言处理能力。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。