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

当孩子们学习语言时，他们吸收了来自许多来源的信息，包括他们所看到的、触摸到的、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、好奇心和挫败感等情绪，这些情绪在他们学习单词的含义时充当着内部和外部的信号。例如，当照顾者不理解孩子说的话时，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，或者它的发音。儿童语言学习的环境是在同一地点进行的，儿童与他们学习语言的人处于同一物理位置，学习是通过口头互动的交流媒介进行的。儿童的信息来源、情绪和口语互动设置与机器学习语言的方式形成了直接对比，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获得灵感，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，并降低人们使用具有自然口语界面的复杂技术的门槛。这个职业项目研究了一种计算机系统学习口语的新方法，它将推进一个理论模型，并改善人和系统的沟通方式。研究将通过从儿童学习语言的方式中获得灵感来改进自然语言处理中的语言建模：他们与他人互动，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是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，极大地丰富模型功效，并将根据人类对机器人行为的感知对情感进行建模，从而增加情感知识。该团队将通过扎根于视觉和机器人状态来添加体现的知识，最后，团队将训练和评估一个使用语言模型的机器人，因为它与人类互动，从他们那里学习语言。这项研究还将产生两个重要的数据集：机器人行为和对这些行为和情感标签的描述，以及机器人与人类互动和学习语言的纵向数据。该项目的目标是(1)建立情感模型；通过互动测试和改进；(2)开发统一的语言模型；(3)让学习语言的人和机器人参与到情感内容中来。该项目由CEISE/IIS/Robust Intelligence Program和NSF建立的激励竞争研究计划(EPSCoR)共同资助。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 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.