Building a comprehensive theory of pragmatic language through large-scale experiments, computation, and neurodiversity

通过大规模实验、计算和神经多样性建立实用语言的综合理论

• 批准号: 2105136
• 负责人: Samantha Floyd
• 金额: $ 13.8万
• 依托单位: Floyd, Samantha B
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105136&HistoricalAwards=false
• 关键词: Building; comprehensive; theory; pragmatic; language

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Edward Gibson and Dr. Evelina Fedorenko, this postdoctoral fellowship award supports an early career scientist examining how non-literal language is understood by humans and machines. Much of human communication is not encoded directly in words: we may say ‘It’s getting late’ to politely indicate that we would like to leave, or call a ballerina a ‘swan’ to capture her grace. This kind of language is called pragmatics and often thought to comprise everything from humor, to white lies, metaphors, implicature, prosody, and more. Although there have been investigations into each of these phenomena individually, it is unknown whether they are supported by the same mechanisms, nor how they relate within the individual, which has implications for neurodivergent individuals facing challenges in communication. And, while current language models show impressive results, little research has explored what kinds of computations might support pragmatic language understanding, which is crucial for success in artificial intelligence. Across both behavioral and computational approaches, the current project will address these limitations by identifying clusters of related pragmatic inferences in humans and exploring how they are computed in models. This project applies new methods to shed light on a unified framework for pragmatic language, with the goal of including neurodivergent individuals as researchers in the process. Large-scale individual-differences studies will develop a comprehensive battery of pragmatic language and expose relationships between abilities (e.g., does understanding white lies correlate best with understanding of irony?). Non-linguistic cognitive assessments will identify which clusters relate to social reasoning, literal language understanding, and executive abilities. By evaluating current language models on non-literal language interpretation, the project with also uncover the learnability, scope, and generalization of language models’ performance on pragmatic language, and will allow us to compare model activity to the clusters found in the human studies. By testing pragmatic language in humans and current computational models, this research will bring us closer to understanding the necessary input and computations for pragmatic language comprehension.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.

该奖项是作为NSF的社会，行为和经济科学博士后研究奖学金（SPRF）计划的一部分提供的。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在Edward吉布森博士和Evelina Fedorenko博士的赞助下，这个博士后奖学金支持一位早期职业科学家研究人类和机器如何理解非字面语言。人类的许多交流并不是直接用语言编码的：我们可能会说“时间不早了”，礼貌地表示我们想离开，或者称芭蕾舞演员为“天鹅”，以捕捉她的优雅。这种语言被称为语用学，通常被认为包括从幽默到白色谎言、隐喻、含义、韵律等等的一切。虽然已经对这些现象中的每一种进行了单独的调查，但尚不清楚它们是否由相同的机制支持，也不知道它们在个体内如何相互关联，这对面临沟通挑战的神经分歧个体有影响。而且，虽然目前的语言模型显示出令人印象深刻的结果，但很少有研究探索什么样的计算可能支持语用语言理解，这对人工智能的成功至关重要。在行为和计算方法中，当前的项目将通过识别人类相关的语用推理集群并探索它们如何在模型中计算来解决这些限制。该项目采用新的方法来阐明语用语言的统一框架，目标是将神经分歧个体作为研究人员纳入该过程。大规模的个体差异研究将开发一个全面的语用语言电池，并揭示能力之间的关系（例如，理解白色谎言与理解讽刺最相关吗？）非语言认知评估将确定哪些集群与社会推理，字面语言理解和执行能力有关。通过评估当前的语言模型对非字面语言的解释，该项目还揭示了语言模型对语用语言的可学习性，范围和概括性，并将使我们能够将模型活动与人类研究中发现的集群进行比较。通过测试人类和当前计算模型中的语用语言，这项研究将使我们更接近理解语用语言理解所需的输入和计算。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。