RI: Small: DaRE: Detection and Recognition of Euphemisms

为了完全理解人类语言，机器需要能够识别和解释包含隐藏含义的表达。这个项目集中在委婉语，温和或间接的短语使用的地方，严厉或更进攻性的。委婉语通常用于掩盖亵渎或以礼貌的方式提及敏感话题，如死亡，性，宗教，残疾或个人关系。人们总是使用委婉语，例如，“消极的病人结果”，“工作之间”，“经济上的财富”，“惩教设施”，“友好的火灾”，或“阳光单位”。不同的文化/语言使用不同的委婉语。委婉语会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中的委婉语，从而为提高人工智能的能力做出贡献。额外的好处包括有趣的新概括的性质委婉语和培训的不同干部的本科生和研究生在一个困难的跨学科问题的高度实用的工作。蒙特克莱尔州立大学是一所西班牙裔服务机构，以其多样化的学生群体和大部分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，委婉语的处理是缺乏在NLP到目前为止。该项目解决了以下问题：1）用于检测和解释委婉语的算法设计，以及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。本文的主要观点是：1）委婉语和其对应的释义在表达情感的强度上存在差异; 2）委婉语和非委婉语的释义是语境敏感的; 3）委婉语比它们所替代的禁忌语更容易被理解。实验测试了深度学习方法捕捉到的委婉语的语言特性以及原因。开发的算法可以检测新的委婉语，以前没有记录在字典中，没有人为干预。委婉语的计算工作是很重要的，以进一步了解如何战略性使用语言可以偏见的人的看法，重要的和高度有争议的行动，也许找到方法如何去偏见的语言模型。这项关于委婉语的工作有助于理解在特定文化中哪些话题是有争议的或敏感的。将该算法应用于历时数据，检测委婉语使用的变化，可以更好地理解文化变化。所产生的语料库对于回答人工智能，NLP，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个自然的方式，使有趣的语言观察委婉语。由于委婉语是一种言语行为，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，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.