Interpretable neural network models of morphological realization

人类语言有复杂的系统，用词法来表达意义和语法信息，词法是构成单词的元素(例如，前缀、后缀和复制的规则)。该项目将开发一种新的计算模型，既具有高度的可解释性，又能够从实例中学习广泛的形态规则。该模型将根据其从包含许多规则和例外的大型自然数据集归纳形态系统的能力进行评估，并在对照实验中从少量证据中概括形态规则方面与人类的表现相匹配。通过融合语言学和其他认知科学领域的见解，该模型将在人类研究和人工智能之间架起一座桥梁。凭借其模块化和透明的设计，该模型将阐明人类学习和推广语言模式的独特能力。该项目将为来自不同背景的多个层次的学生提供语言学、认知心理学、人工智能和数据科学方面的跨学科培训机会。大量语言学研究已经确定了形态系统的一般属性以及它们在不同语言中变化的受限方式，而人工智能的最新进展已经产生了可以在最小监督下学习形态的计算模型。然而，这两种方法在很大程度上是相互孤立地发展起来的。该项目将开发一种新的可理解和可解释的深度神经网络，因为它的表示和操作是语言理论离散符号组件的连续版本，并且可以用域通用学习算法(例如随机梯度下降)从肯定证据中归纳出广泛的形态实现规则。该模型将在自然语言处理中常见的大型自然数据集上进行评估，并根据新的微型人工语法实验的结果进行评估，这些实验涉及固定、插入和重复。该项目旨在证明，当深层神经网络具有反映符号语言理论的基本表示和操作的模块化设计时，它们可以为语言结构和习得的研究做出变革性的贡献。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。

Human languages have complex systems for expressing meaning and grammatical information with morphology, the elements which make up words (e.g., rules of prefixing, suffixing, and copying). The project will develop a novel computational model that is both highly interpretable and capable of learning a broad range of morphological rules from examples. The model will be evaluated on its ability to induce morphological systems from large naturalistic data sets containing many rules and exceptions and to match human performance in generalizing morphological rules from small amounts of evidence in controlled experiments. By incorporating insights from linguistics and other areas of cognitive science, the model will provide a bridge between the studies of human and artificial intelligence. In virtue of its modular and transparent design, the model will shed light on the uniquely human capacity to learn and extend linguistic patterns. The project will provide interdisciplinary training opportunities in linguistics, cognitive psychology, artificial intelligence, and data science for students at many levels and from diverse backgrounds.A large body of research in linguistics has identified the general properties of morphological systems and the restricted ways in which they vary across languages, while recent advances in artificial intelligence have given rise to computational models that can learn morphology with minimal supervision. These two approaches, however, have been developed largely in isolation from one another. The project will develop a novel kind of deep neural network that is understandable and interpretable, in the sense that its representations and operations are continuous versions of the discrete symbolic components of linguistic theory, and that can induce a broad range of morphological realization rules from positive evidence with domain-general learning algorithms (e.g., stochastic gradient descent). The model will be evaluated on large naturalistic data sets that are common in natural language processing and on results from new miniature artificial grammar experiments on infixation, intercalation, and reduplication. The project aims to demonstrate that deep neural networks can make transformative contributions to the study of language structure and acquisition when they have a modular design that mirrors the fundamental representations and operations of symbolic linguistic theory.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.