Inferring Implicit Comparison Classes in Natural Language Understanding

这一奖项是作为NSF的社会、行为和经济科学(SBE)博士后研究奖学金(SPRF)计划和SBE的感知、行动和认知计划的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项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。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在麻省理工学院罗杰·利维博士的赞助下，这一博士后奖学金奖项支持一位研究儿童对语境敏感语言理解的早期职业科学家。学习语言是具有挑战性的，因为单词在不同的语境中有不同的意义。形容词“大”的意思是一个物体的大小大于某个标准，但这个标准取决于上下文(例如，一只大鞋子比一座大建筑小得多)。现有的研究表明，当孩子们开始说出大这个词的时候，他们已经理解了它的上下文敏感性(大鞋子和大建筑)。理解儿童可获得并使用的线索，以形成对句子的上下文敏感解释，将为语言发展的理论和模型提供参考。此外，用精确的数学模型形式化上下文敏感语言的使用和理解也将有助于我们建立以更像人类的方式理解语言的机器。该项目包括一系列实验、语料库和计算建模研究，以阐明人类令人难以置信的灵活学习和使用语言的能力背后的表征。在目标1下，我们扩展了最先进的概率模型来解释上下文敏感的话语(例如，“Big”)，以解决语义相同但句法不同的话语(例如，“That Great Dane is Big”与“That Great Dane is Big”)。“那是一只大的大丹狗”)可以为相关的标准或比较提供线索(例如，狗的大与大丹狗的大)。在目标2下，我们考察了幼儿和他们的照顾者之间对话的自然视频语料库，以揭示儿童听者可以收集的感知、语言和参照线索，以构建相关的比较来解释上下文敏感的形容词，如大的。在目标3下，我们检验了这些线索对成人和4-5岁儿童关于相关比较的推论的因果影响。在成人和儿童身上测试我们的假设有助于了解理解语境敏感语言的发展起源。这项工作在语言习得和计算认知科学领域之间建立了新的联系，同时加深了我们对两者的理解。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。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Perception, Action, and Cognition 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. Roger Levy at MIT, this postdoctoral fellowship award supports an early career scientist investigating children's understanding of context-sensitive language. Learning language is challenging because words mean different things in different contexts. The adjective 'big' conveys that the size of an object is greater than some standard, but what that standard is depends on the context (e.g., a big shoe is a lot smaller than a big building). Extant research suggests that by the time children start producing the word 'big', they already understand its context-sensitivity (big shoe vs. big building). Understanding the cues available to and used by a child to form a context-sensitive interpretation of a sentence will inform theories and models of language development. In addition, formalizing with precise mathematical models how context-sensitive language is used and understood will also help us build machines that understanding language in more humanlike ways. The project encompasses a series of experimental, corpus, and computational modeling studies to elucidate the representations that underlie the incredible human capacity to learn and use language flexibly. Under Objective 1, we extend state-of-the-art probabilistic models for interpreting context-sensitive utterances (e.g. 'big') to address how semantically identical but syntactically different utterances (e.g., "That Great Dane is big" vs. "That is a big Great Dane") could provide cues to the relevant standards or comparisons (e.g., big for a dog vs. big for a Great Dane). Under Objective 2, we examine naturalistic video corpora of conversations between young children and their care-givers to uncover the perceptual, linguistic, and referential cues that the child listener could recruit to construct the relevant comparisons for interpreting context-sensitive adjectives like 'big'. Under Objective 3, we test the causal influence of such cues on adult and 4- to 5-year-old's inferences about the relevant comparisons. Testing our hypotheses in both adults and young children sheds light on the developmental origins of understanding context-sensitive language. This work makes new connections between the fields of language acquisition and computational cognitive science while furthering our understanding of both.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.