CAREER: Integrating Interaction, Embodiment, and Emotion to Transform Language Models

职业：整合交互、体现和情感来转变语言模型

• 批准号: 2140642
• 负责人: Casey Kennington
• 金额: $ 49.75万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140642&HistoricalAwards=false
• 关键词: CAREER; Integrating; Interaction; Embodiment; Emotion

When children learn language, they incorporate information from many sources including what they see, touch, smell, hear, and how they feel. Before children can speak, they feel emotions such as anger, joy, curiosity, and frustration and these emotions act as internal and external signals as they learn what words mean. For example, a child might show frustration when a caregiver does not understand what the child is saying, which in turn helps the child change what they understand a word or phrase to mean or how it is pronounced. The setting in which child language learning takes place is co-located where children are in the same physical location as the people they are learning language from, and the learning is through the communicative medium of spoken interaction. The information sources, emotions, and spoken interaction setting for children are in direct contrast to how machines learn language, which usually involves some kind of computational model that is given large amounts of text to learn from. This project aims to take inspiration from how children learn language in order to understand how to improve the models and methods of machines that learn language. Improved language learning in machines will enable machines to communicate with people more quickly, clearly, safely, and in ways that lower barriers for people to use complex technology with a natural spoken language interface. This CAREER project examines a novel approach for computer systems to learn spoken language that will advance a theoretical model and improve how people and systems communicate. Research will improve language modeling in natural language processing by taking inspiration from how children learn language: they interact with others to learn words that denote physical entities and events, and, like all humans, often respond emotionally and embody how they feel in their behavior, whereas researchers currently largely train language models only on static text. The research team will use two robotic platforms for the research and significantly enrich model efficacy and will add knowledge of emotion by modeling it based on human perceptions of robot behaviors. The team will add embodied knowledge by grounding into vision and robot states, and finally, the team will train and evaluate a robot that uses the language model as it interacts with humans to learn language from them. The study will also result in two important datasets: robot behaviors with accompanying descriptions of those behaviors and emotion labels, and longitudinal data of robots interacting and learning language from humans. The objectives are to (1) Model emotion; test, and refine through interaction, (2) Develop a unified language model, and (3) Engage people and robots that learn language with emotional content.This project is jointly funded by the CISE/IIS/Robust Intelligence Program and the NSF Established Program to Stimulate Competitive Research (EPSCoR).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.

当孩子学习语言时，他们会吸收来自许多来源的信息，包括他们所看到的、触摸的、闻到的、听到的以及他们的感觉。在孩子会说话之前，他们会感受到愤怒、喜悦、好奇和沮丧等情绪，这些情绪在他们学习单词含义时充当内部和外部信号。例如，当看护者不明白孩子在说什么时，孩子可能会表现出沮丧，这反过来又有助于孩子改变他们对单词或短语的理解或发音。儿童语言学习发生的环境是在同一地点，儿童与他们学习语言的人处于同一物理位置，并且学习是通过口语互动的交流媒介进行的。儿童的信息源、情感和口语互动设置与机器学习语言的方式形成鲜明对比，机器学习语言通常涉及某种计算模型，需要从大量文本中学习。该项目旨在从儿童学习语言的方式中汲取灵感，以了解如何改进学习语言的机器的模型和方法。机器语言学习的改进将使机器能够更快、更清晰、更安全地与人交流，并降低人们使用自然口语界面的复杂技术的障碍。该职业项目研究了一种计算机系统学习口语的新方法，该方法将推进理论模型并改善人与系统的沟通方式。研究将通过从儿童学习语言的方式中汲取灵感来改进自然语言处理中的语言建模：他们与他人互动以学习表示物理实体和事件的单词，并且像所有人类一样，经常做出情感反应并在行为中体现他们的感受，而研究人员目前主要仅在静态文本上训练语言模型。研究团队将使用两个机器人平台进行研究，显着丰富模型功效，并将根据人类对机器人行为的感知进行建模，以增加情感知识。该团队将通过深入研究视觉和机器人状态来添加具体知识，最后，该团队将训练和评估一个机器人，该机器人在与人类交互时使用该语言模型来向人类学习语言。该研究还将产生两个重要的数据集：机器人行为以及这些行为和情感标签的伴随描述，以及机器人与人类互动和学习语言的纵向数据。目标是 (1) 模拟情绪；测试，并通过交互进行完善，(2) 开发统一的语言模型，(3) 让人和机器人学习带有情感内容的语言。该项目由 CISE/IIS/Robust Intelligence 计划和 NSF 既定计划联合资助刺激竞争性研究 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。