CRII: RI: Explainable Recognition of Social Relationships from People's Linguistic Interactions

人与人之间的社会关系不仅会影响人们所说的话，还会通过尊重、礼貌或团结等方面的语言线索来影响信息的表达方式。这些文体方面不仅反映了特定的社会背景，而且还会影响收件人对信息本身的解释。这些关系往往是多方面的，并伴随着不同的语言期望。例如，虽然家长、教授和警察都可能选择权威的语言，但在他们与儿童、学生和社区的各自互动中，他们的语言反映了他们如何与他人建立关系的更广泛的语言期望。该项目旨在通过建立新的技术来研究社会关系及其对语言的影响，该技术识别关系的语言线索，从而显著改进语音助手、翻译和摘要等日常交互语言技术，并通过提高机器理解仅靠文字无法获得的交流的社会方面的能力，导致更自然的人机交互。这项研究还将支持博士和本科生的跨学科队列，并利用开放获取的材料开发一门研究生水平的计算社会语言学课程。这项CEISE研究启动计划(CRII)通过开发新的算法、方法和数据集来调查社会关系及其对语言的影响，建立在社会语言学理论的基础上，为研究语言和关系在现实社会环境中的相互作用创造一个新的框架。该项目包括两个主要阶段。第一阶段开发了新的深度学习方法，从对话中的语言线索准确地推断出人们的关系。为了支持这一努力，它还从现实世界和电影、电视和文学中的虚构背景构建了一个新的、大规模的人际对话语料库。第二阶段建立了可解释的交流计算模型，该模型解释了一种关系是如何通过交流中的语言选择来表示的，同时也解开了对话伙伴身份的语言影响。这些模型能够测试和扩展关于关系和社会组织的社会科学理论。此外，该项目还引入了一项新的自然语言理解任务，用于识别仅在某些上下文中具有攻击性的信息，其中语言线索违反了关系的社会规范，有助于进一步改进技术，以改善公众的在线体验。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和更广泛的影响审查标准进行评估，被认为值得支持。

Social relationships between people influence not only what is said but how a message is said through linguistic cues for aspects like respect, politeness, or solidarity. These stylistic aspects not only reflect a particular social context, but can also affect how the recipient interprets the message itself. These relationships are often multifaceted and come with different linguistic expectations. For example, while parents, professors, and police may all choose the language of authority, in their respective interactions with children, students, and the community, their language reflects a broader set of linguistic expectations in how they relate to others. This project aims to study social relationships and their effect on language by building new technology that recognizes the linguistic cues of relationships resulting in significant improvements in everyday interactive language technologies such as voice assistants, translation, and summarization and lead to more natural human-machine interactions by improving the ability of machines to understand social aspects of communication that cannot be obtained from the words alone. This research will also support an interdisciplinary cohort of PhD and undergraduate students and the development of a graduate-level course on Computational Sociolinguistics with open access materials.This CISE Research Initiation Initiative (CRII) project investigates social relationships and their impact on language by developing new algorithms, methods, and datasets, building upon sociolinguistics theory to create a new framework for studying the interplay of language and relationships in real-world social environments. This project contains two main phases. The first phase develops new deep learning methods to accurately infer people's relationship from linguistic cues in their conversation. To support this effort, it also constructs a new, massive corpus of interpersonal conversations from real-world settings and from fictional settings in movies, television, and literature. The second phase builds interpretable computational models of communication that explain how a relationship is signaled through linguistic choices in communication, while also disentangling the linguistic influences of the conversational partners' identities. These models enable testing and extending social science theories about relationship and social organization. Further, this project also introduces a new natural language understanding task for identifying messages that are offensive only in certain contexts, where the linguistic cues violate social norms for a relationship, helping to further improve technologies to better the public's online experiences.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.