Construction Grammar and Language Models

TL;DR

This paper explores how deep learning language models can access and represent constructional knowledge, highlighting opportunities for collaboration between NLP and Construction Grammar to enhance linguistic understanding.

Contribution

It provides a comprehensive overview of computational approaches, especially deep learning models, and investigates their potential to capture constructional information in language.

Findings

Deep learning models show evidence of constructional knowledge emergence.

Construction Grammar can inform the development of more linguistically aware models.

Challenges remain in modeling certain aspects of Construction Grammar with current models.

Abstract

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.