A new approach to computational model evaluation: Benchmarking against infant experimental data

这个项目是关于确定什么时候英语和西班牙语学习的婴儿变得敏感的辅音和元音的顺序在他们的母语。例如，英语单词可以开始以“st”开头（如“state”或“stamp”），而西班牙语单词则不能。研究人员将利用婴儿研究的实验结果来评估计算模型所做的预测，这些模型代表了关于婴儿如何学习语言的不同假设。这种比较将提供对婴儿使用的特定机制的洞察。此外，揭示婴儿何时调整到声音排序限制的详细时间轴将使我们能够更好地理解一些患有沟通障碍的儿童在学习声音排序时遇到困难的根源。该项目的第二个成果是更多地将讲西班牙语的人口纳入发展研究，这对于美国不断变化的人口统计数据非常重要。多模型架构通常实现类似的成功水平时，对成人扬声器的性能基准。在这个项目中，计算模型将直接针对婴儿的行为进行评估，以提供额外的标准来区分它们。更具体地说，研究人员将评估婴儿对母语中声音排序限制的知识是否可以通过不同的分词计算模型的输出来支持。除了提供获得声音定序限制的详细时间轴和更好地理解婴儿使用的发育机制外，这个项目也将作为未来研究的一个模板，这些研究的目的是将计算模型直接与婴儿行为进行比较。这个奖项反映了国家科学基金会的法定使命，并被认为是值得通过利用基金会的智力价值和更广泛的评估来支持的影响审查标准。

This project is about determining when English- and Spanish-learning infants become sensitive to the sequencing of consonants and vowels in their native language. For example, English words can begin with ‘st’ (as in ‘state’ or ‘stamp’), while Spanish words cannot. The researchers will use experimental findings from studies on infants to evaluate predictions made by computational models that represent different hypotheses about how infants learn language. This comparison will provide insight into the specific mechanisms used by infants. In addition, uncovering the detailed timeline of when infants tune into sound sequencing restrictions will enable a better understand the roots of the difficulties some children with communication disorders have in learning sound sequencing. A secondary outcome of this project is the greater inclusion of Spanish-speaking populations in developmental research, which is important given the changing demographics in the United States.In recent years, many computational models have been proposed to explain how infants learn language. Multiple model architectures typically achieve similar levels of success when benchmarked against the performance of adult speakers. In this project, computational models will be evaluated directly against infant behavior in order to provide additional criteria for distinguishing between them. More specifically, the researchers will evaluate whether infants’ knowledge of sound sequencing restrictions in their native language can be supported by the output of different computational models of word segmentation. In addition to providing a detailed timeline of the acquisition of sound sequencing restrictions and a better understanding of the developmental mechanisms used by infants, this project will also serve as a template for future studies that aim to compare computational models directly against infant behavior.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.