Representing and learning stress: Grammatical constraints and neural networks

语言是非常复杂的系统，语言学家和计算机科学家已经投入了相当大的努力来发展表示这些复杂系统的方法，以及学习给定语言系统的计算方法。这种努力是由更好地理解人类认知和建立更好的语言技术的愿望驱动的。这个项目利用语言学和计算机科学的理论和方法来研究单词重音的学习，即单词中音节相对突出的模式。世界上各种语言的重音系统都被很好地描述了，关于它们是如何表现的，存在着相互竞争的语言学理论。本项目运用计算机科学的学习方法，寻找新的证据来区分相互竞争的语言学理论。它还研究了在计算机科学中发展起来的语言表示系统，这些系统很少受到语言学家（神经网络）的关注。这项研究将由一所公立大学的语言学本科生和研究生参与。语言学的女生比例远高于计算机科学，该项目旨在解决STEM中的性别失衡问题。从语言学的角度来看，学习压力涉及学习隐藏结构，即在观察数据中不存在的表征部分，必须由学习者推断。一个给定的突出音节模式通常与多个韵律表示相一致。本项目中使用的隐结构学习方法采用了期望最大化的一般技术，在标准测试集的试点工作中取得了良好的效果。有趣的是，这个学习者在测试集中失败的许多语言实际上是跨语言未经证实的。该项目扩展了测试语言集，以包括更多跨语言发现的系统范围，并进一步探索类型学差异具有学习解释的可能性。它通过比较它们对已证实系统的学习的支持程度，以及它们是否有助于解释类型差距，来比较关于压力放置的约束的假设。试点工作还发现，神经网络可以学习数据的泛化表示；该项目正在进一步测试这种方法。在这个项目中开发的所有软件都是免费提供的，世界语言的重音系统数据库也是如此。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Languages are systems of remarkable complexity, and linguists and computer scientists have devoted considerable effort to the development of methods for representing those complex systems, as well as computational methods for learning the system of a given language. This effort is driven by the desires to better understand human cognition, and to build better language technologies. This project draws on the theories and methods of both linguistics and computer science to study the learning of word stress, the pattern of relative prominence of the syllables in a word. The stress systems of the world's languages are relatively well described, and there are competing linguistic theories of how they are represented. This project applies learning methods from computer science to find new evidence to distinguish the competing linguistic theories. It also examines systems of language representation that have been developed in computer science and have received relatively little attention by linguists (neural networks). The research will engage undergraduate and graduate linguistics students at a public university. Linguistics has a much higher proportion of female students than computer science, and this project aims to address gender imbalance in STEM. From a linguistic perspective, learning stress involves learning hidden structure, parts of the representation that are not present in the observed data and that must be inferred by the learner. A given pattern of prominence over syllables is often consistent with multiple prosodic representations. The approach to hidden structure learning used in this project applies the general technique of Expectation Maximization, which in pilot work achieved good results on a standard test set. Intriguingly, many of the languages that this learner failed on in the test set are ones that are in fact cross-linguistically unattested. This project expands the set of tested languages to include more of the range of systems found cross-linguistically, and further explores the possibility that typological gaps have learning explanations. It compares hypotheses about the constraints responsible for stress placement by comparing how well they support the learning of attested systems, and whether they can help explain typological gaps. Pilot work also found indications that a neural network could learn generalizable representations of the data; the project is further testing this method. All of the software developed in this project is being made freely available, as is a database of the stress systems of the world’s languages.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.