CAREER: Harnessing Interpersonal Common Sense for Social Grounding in Natural Language Processing

在与其他个体互动时，人类的行为在很大程度上取决于互动者之间的关系。例如，当与社会地位较高的人交谈时，人们经常通过模仿对方的语言风格来表现出语言的协调。即使是12到18个月大的孩子，也可以根据和谁在一起的情况来调整自己的行为。换句话说，人类拥有并使用人际常识：关于不同人际关系中可接受的行为的常识知识，并将其用于日常互动。相比之下，计算机缺乏这种人际常识。为了让计算机模拟人类表现出的社会行为，并以类似人类的方式运行，它们需要人际常识。近年来，随着人工对话代理和机器人等技术在我们的日常生活中变得越来越普遍，为计算机配备这种能力的需求变得更加重要。这个职业项目的目标是向计算机中灌输人际常识知识和推理能力。为了实现这一目标，该项目开发了存储人际常识知识的资源，以及利用这些资源设计更了解人类世界普遍存在的社会动态的计算机系统的技术。该项目涉及计算机科学内外的研究人员和学生的跨学科努力。它包括为计算机科学、语言学和心理学的研究生和本科生开发跨学科课程和研讨会。它还包括组织研讨会、演示和讲座，以吸引女性等历史上代表性不足的少数民族进入计算机科学。该项目开发的技术将帮助自然语言处理(NLP)系统获得人际常识并将其纳入其运作中。该项目的努力分为三个方面。第一个开发了人类注释的人际常识实例的知识库。给定包含两个实体之间交互的文本摘录，知识库将包含关于这两个实体的人际推理的各个方面的注释。第二种方法是不断地、自动地扩展知识库并将其推广到看不见的情况。这些方法是基于多任务学习，以便自动和联合地推断人际常识的各个方面，包括关于看不见的情景的常识。反过来，这些推理也被用来改进半监督框架中的方法。第三种是利用这些人际常识来改进数字人文领域的对话生成、摘要和信息抽取等自然语言处理系统。这些自然语言处理系统从知识库中学习人际常识，并利用它，同时专注于文本中提到的实体，以改进下游任务。每一次推进都伴随着对已开发技术的广泛和持续的评估。这项研究将产生公开可用的资源、数据和技术，供该领域的其他人使用和培训他们的系统。总体而言，该项目推动了研究朝着设计更具社会认知性和更人性化的NLP系统的方向发展。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和更广泛的影响审查标准进行评估，被认为值得支持。

When interacting with other individuals, human behavior significantly depends on the relationship between the interactants. For example, when speaking to someone with a higher social status, people often exhibit language coordination by mimicking the linguistic style of the other speaker. Even children as young as 12 to 18-months old can adjust their behavior depending on who they are with. In other words, humans possess and employ interpersonal common sense: common-sense knowledge of the behavior acceptable in different interpersonal relationships, and use it in their day-to-day interactions. In contrast, computers lack this interpersonal common-sense knowledge. In order for computers to model the social behavior exhibited by humans and operate in a human-like manner, they need interpersonal common sense. This need to equip computers with this capacity has become even more important in recent times with technology, such as artificial conversational agents and robots, becoming increasingly pervasive in our day-to-day lives. The goal of this CAREER project is to instill interpersonal common-sense knowledge and reasoning capabilities in computers. To achieve this goal the project develops resources that store interpersonal common-sense knowledge together with techniques to leverage them for designing computer systems that are more aware of social dynamics prevalent in the human world. The project involves interdisciplinary efforts by researchers and students from within and outside of Computer Science. It includes developing interdisciplinary courses and seminars for graduate and undergraduate students in Computer Science, Linguistics, and Psychology. It also involves organizing workshops, demos and talks for attracting historically underrepresented minorities like women to computer science. This project develops technologies that will help Natural Language Processing (NLP) systems to acquire and incorporate interpersonal common sense in their functioning. The project’s efforts are divided into three thrusts. The first develops a knowledge base of human-annotated instances of interpersonal common-sense knowledge. Given a text excerpt containing interaction between two entities, the knowledge base will contain annotations about various facets of interpersonal inferences about the two entities. The second develops methods for continually and automatically expanding the knowledge base and generalizing to unseen situations. These methods are based on multi-task learning in order to automatically and jointly infer the various facets of interpersonal common sense, including those about unseen scenarios. The inferences, in turn, are also used to improve the methods in a semi-supervised framework. The third utilizes this interpersonal common-sense knowledge to improve NLP systems like those for dialog generation, summarization and information extraction for digital humanities. These NLP systems learn about interpersonal common sense from the knowledge base and utilize it while focusing on the entities mentioned in the text in order to improve the downstream task. Each thrust is accompanied by extensive and continual evaluations of the developed techniques. The research will result in publicly available resources, data and technologies for others in the field to use and train their systems on. Overall, this project pushes research in the direction of designing more socially cognizant and human-like NLP 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.