Recent progress in deep learning and natural language processing has given rise to powerful models that are primarily trained on a cloze-like task and show some evidence of having access to substantial linguistic information, including some constructional knowledge. This groundbreaking discovery presents an exciting opportunity for a synergistic relationship between computational methods and Construction Grammar research. In this chapter, we explore three distinct approaches to the interplay between computational methods and Construction Grammar: (i) computational methods for text analysis, (ii) computational Construction Grammar, and (iii) deep learning models, with a particular focus on language models. We touch upon the first two approaches as a contextual foundation for the use of computational methods before providing an accessible, yet comprehensive overview of deep learning models, which also addresses reservations construction grammarians may have. Additionally, we delve into experiments that explore the emergence of constructionally relevant information within these models while also examining the aspects of Construction Grammar that may pose challenges for these models. This chapter aims to foster collaboration between researchers in the fields of natural language processing and Construction Grammar. By doing so, we hope to pave the way for new insights and advancements in both these fields.

翻译：深度学习和自然语言处理的最新进展催生了强大模型，这些模型主要基于完形填空式任务进行训练，并展现出掌握大量语言信息（包括部分构式知识）的迹象。这一突破性发现为计算方法与构式语法研究之间的协同关系提供了令人兴奋的契机。本章探讨了计算方法与构式语法相互作用的三种不同路径：(i) 用于文本分析的计算方法，(ii) 计算构式语法，以及(iii) 深度学习模型，尤其聚焦于语言模型。我们简要论述前两种方法作为应用计算方法的背景基础，随后对深度学习模型进行通俗易懂但全面的概述，同时回应构式语法研究者可能存在的疑虑。此外，我们深入探讨了这些模型中构式相关信息的涌现实验，并审视了构式语法中可能对模型构成挑战的方面。本章旨在促进自然语言处理与构式语法领域研究者之间的合作，以期开辟这两个领域的新见解与进展。