RI: Small: DaRE: Detection and Recognition of Euphemisms

RI：小：DaRE：委婉语的检测和识别

• 批准号: 2226006
• 负责人: Anna Feldman
• 金额: $ 56.41万
• 依托单位: Montclair State 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: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226006&HistoricalAwards=false
• 关键词: RI; Small; DaRE; Detection; Recognition

To fully understand human language, machines need to be able to recognize and interpret expressions that contain hidden meanings. This project concentrates on euphemisms, mild or indirect phrases used in place of harsher or more offensive ones. Euphemisms are often used to mask profanity or refer to sensitive topics such as death, sex, religion, disability, or personal relationships in a polite way. People use euphemisms all the time, e.g., 'negative patient outcome', 'between jobs', 'financially fortunate', 'correctional facility','friendly fire', or 'sunshine unit'. Different cultures/languages use different euphemisms. Euphemisms change over time. Machines that process human language do not understand euphemisms yet. This project is devoted to making machines understand euphemisms in different languages, and therefore contributing to improving the capabilities of artificial intelligence. Additional benefits include interesting new generalizations about the nature of euphemisms and the training of a diverse cadre of undergraduate and graduate students in highly practical work on a difficult interdisciplinary problem. Montclair State University, a Hispanic Serving Institution, is known for its diverse student population and a large proportion of first-generation college students. Montclair State University puts great emphasis on justice and inclusivity in academia. This project is not an exception.Detecting and interpreting figurative language is a rapidly growing area in Natural Language Processing (NLP). Unfortunately, the processing of euphemisms is lacking in NLP thus far. The project addresses the following: 1) algorithm design for detecting and interpreting euphemisms, and 2) interpretability of black-box neural models by creating a series of new datasets and tasks that explore the embedding space of transformer language models for euphemism recognition. The key insights are 1) euphemistic expressions and their paraphrased counterparts differ in the strength of the sentiment they convey; 2) euphemistic and non-euphemistic interpretation is context-sensitive; 3) euphemisms are vaguer than the taboo expressions they substitute. The experiments test what linguistic properties of euphemisms the deep learning approaches capture and why. The algorithm developed can detect new euphemisms, not previously recorded in dictionaries, without human intervention. The computational work on euphemisms is important to further the understanding of how strategic use of language can bias people's perceptions of important and highly contentious actions and perhaps find ways how to de-bias language models. This work on euphemisms helps understand what topics are controversial or sensitive in a specific culture. Applying the algorithm to diachronic data and detecting the change in euphemism usage leads to a better understanding of culture changes. The corpora produced are useful for answering questions at the intersection of AI, NLP, linguistics, cultural anthropology, and social psychology. The range of languages provides a natural way of making interesting linguistic observations about euphemisms. Since euphemisms are a form of verbal behavior, finding a way to detect and interpret euphemisms automatically may lead to a better understanding of human behavior in general.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.

为了充分理解人类语言，机器需要能够识别和解释包含隐藏含义的表达。该项目专注于委婉语、温和或间接的短语，用来代替更严厉或更具攻击性的短语。委婉语通常用于掩盖脏话或以礼貌的方式提及敏感话题，例如死亡、性、宗教、残疾或个人关系。人们总是使用委婉语，例如“患者的负面结果”、“工作间隙”、“经济上幸运”、“惩教所”、“友军火力”或“阳光单位”。不同的文化/语言使用不同的委婉语。委婉语随着时间的推移而变化。处理人类语言的机器还不能理解委婉语。该项目致力于让机器理解不同语言的委婉语，从而为提高人工智能的能力做出贡献。其他好处包括对委婉语本质的有趣的新概括，以及对多元化的本科生和研究生干部进行培训，以解决困难的跨学科问题的高度实践工作。蒙特克莱尔州立大学是一所西班牙裔服务机构，以其多元化的学生群体和大量的第一代大学生而闻名。蒙特克莱尔州立大学非常重视学术界的公正和包容性。这个项目也不例外。检测和解释比喻语言是自然语言处理 (NLP) 中一个快速发展的领域。不幸的是，到目前为止，NLP 还缺乏委婉语的处理。该项目解决以下问题：1）用于检测和解释委婉语的算法设计，2）通过创建一系列新的数据集和任务来探索用于委婉语识别的 Transformer 语言模型的嵌入空间，从而实现黑盒神经模型的可解释性。主要见解是：1）委婉表达和释义表达的不同之处在于它们所传达的情感强度； 2）委婉和非委婉解释是上下文相关的； 3）委婉语比它们所替代的禁忌用语更加模糊。这些实验测试了深度学习方法捕获委婉语的哪些语言特性以及原因。所开发的算法可以检测以前未记录在词典中的新委婉语，无需人工干预。委婉语的计算工作对于进一步理解语言的策略性使用如何影响人们对重要和高度争议的行为的看法具有重要意义，并可能找到消除语言模型偏见的方法。这项关于委婉语的工作有助于了解哪些话题在特定文化中是有争议或敏感的。将算法应用于历时数据并检测委婉语使用的变化可以更好地理解文化变化。生成的语料库对于回答人工智能、自然语言处理、语言学、文化人类学和社会心理学的交叉问题非常有用。语言的范围提供了一种对委婉语进行有趣的语言观察的自然方式。由于委婉语是言语行为的一种形式，因此找到一种自动检测和解释委婉语的方法可能会更好地理解人类的一般行为。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

A Report on the Euphemisms Detection Shared Task

• DOI: 10.48550/arxiv.2211.13327
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Patrick Lee;Anna Feldman;J. Peng

Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

第三届形象语言处理（FLP）研讨会论文集

• 发表时间: 2022
• 期刊: Association for Computational Linguistics
• 作者: Ghosh, Debanjan

NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification

• DOI: 10.48550/arxiv.2305.10971
• 发表时间: 2023-05
• 期刊: ArXiv
• 作者: Iyanuoluwa Shode;David Ifeoluwa Adelani;J. Peng;Anna Feldman