[Paper Review] Construction Grammar and Artificial Intelligence

Katrien Beuls, Paul Van Eecke|arXiv (Cornell University)|Aug 31, 2023

Natural Language Processing Techniques4 citations

TL;DR

This paper argues that construction grammar (CxG) and artificial intelligence (AI) share a foundational commitment to modeling language as a dynamic, usage-based, and communicatively grounded system. It demonstrates how AI techniques enhance CxG's operationalization and scalability, while CxG insights advance AI's goal of building truly intelligent, language-capable agents, with mutual benefits evident in frameworks like Fluid Construction Grammar and the FrameNet project.

ABSTRACT

In this chapter, we argue that it is highly beneficial for the contemporary construction grammarian to have a thorough understanding of the strong relationship between the research fields of construction grammar and artificial intelligence. We start by unravelling the historical links between the two fields, showing that their relationship is rooted in a common attitude towards human communication and language. We then discuss the first direction of influence, focussing in particular on how insights and techniques from the field of artificial intelligence play an important role in operationalising, validating and scaling constructionist approaches to language. We then proceed to the second direction of influence, highlighting the relevance of construction grammar insights and analyses to the artificial intelligence endeavour of building truly intelligent agents. We support our case with a variety of illustrative examples and conclude that the further elaboration of this relationship will play a key role in shaping the future of the field of construction grammar.

Motivation & Objective

• To establish the historical and conceptual continuity between construction grammar and artificial intelligence, rooted in shared views on language as communicative, usage-based, and grounded in cognition.
• To demonstrate how AI techniques—particularly logic programming, neural heuristics, and evolutionary modeling—enhance the operationalization, validation, and scaling of constructionist linguistic models.
• To show how construction grammar insights, such as frame semantics and constructional networks, contribute to building more robust, grounded, and generalizable AI systems capable of human-like language understanding and production.
• To advocate for deeper integration between CxG and AI as essential for advancing both fields toward truly intelligent, human-like language processing systems.
• To illustrate this synergy through case studies, including Fluid Construction Grammar, the Talking Heads experiment, and FrameNet, highlighting bidirectional influence and shared cognitive mechanisms.

Proposed method

• Utilizes historical and textual analysis of key works in CxG and AI (e.g., Fillmore, Wilensky, Norvig, Schank, Minsky) to trace shared conceptual roots in communication, grounding, and usage-based learning.
• Applies computational frameworks such as Fluid Construction Grammar (FCG), which uses logic programming and unification to model bidirectional language processing and constructional alignment.
• Employs evolutionary and developmental robotics approaches (e.g., Steels’ Talking Heads experiment) to simulate the emergence of linguistic structure through interaction, grounded in perception and action.
• Integrates frame semantics and FrameNet as a bridge between linguistic theory and AI applications, enabling knowledge-rich language understanding.
• Leverages neural heuristics and meta-layer problem solving (e.g., Van Eecke et al.) to scale construction grammar processing, improving generalization and efficiency.
• Uses multilevel alignment and constructional dependency models (e.g., van Trijp & Steels) to maintain systematicity and coherence in evolving linguistic systems.

Experimental results

Research questions

• RQ1 How do construction grammar and artificial intelligence share a common conceptual foundation in modeling language as a communicative, usage-based, and grounded system?
• RQ2 In what ways can AI techniques such as logic programming, neural heuristics, and evolutionary modeling enhance the operationalization and scalability of construction grammar frameworks?
• RQ3 How do insights from construction grammar—particularly frame semantics and constructional networks—contribute to the development of more robust and generalizable AI systems for language understanding and production?
• RQ4 What evidence exists for bidirectional influence between CxG and AI, and how do shared mechanisms like constructional alignment and perceptual grounding support this mutual advancement?
• RQ5 How can computational construction grammars like Fluid Construction Grammar be used to model the co-acquisition of syntax and semantics in a grounded, interactive environment?

Key findings

• Construction grammar and artificial intelligence share a deep historical and conceptual lineage, rooted in the view of language as a dynamic, communicative, and usage-based system grounded in cognition and perception.
• AI techniques such as logic programming and neural heuristics significantly enhance the scalability and operationalization of construction grammar models, enabling efficient parsing, production, and generalization.
• The FrameNet project, inspired by Fillmore’s frame semantics, exemplifies how construction grammar insights have directly influenced AI systems for natural language understanding, becoming a standard component in early AI textbooks.
• Evolutionary robotics experiments like the Talking Heads project demonstrate that linguistic structures—including syntax and semantics—can emerge through interaction, supporting the idea that language is learned through situated, communicative experience.
• Computational frameworks such as Fluid Construction Grammar support bidirectional processing and constructional alignment, enabling agents to both understand and produce language using the same representations.
• The integration of construction grammar with AI advances the goal of building truly intelligent agents by embedding language in broader cognitive processes, including reasoning, vision, and social coordination.