Linguistic theories and models of natural language can be divided into two categories, depending on whether they represent and process linguistic information numerically or symbolically. Numerical representations, such as the embeddings that are at the core of today’s large language models, have the advantage of being learnable from textual data, and of being robust and highly scalable. Symbolic representations, like the ones that are commonly used to formalise construction grammar theories, have the advantage of being compositional and interpretable, and of supporting sound logic reasoning. While both approaches build on very different mathematical frameworks, there is no reason to believe that they are incompatible. In the present paper, we explore how numerical, in casu distributional, representations of linguistic forms, constructional slots and grammatical categories can be integrated in a computational construction grammar framework, with the goal of reaping the benefits of both symbolic and numerical methods.

Constructionist approaches to language posit that all linguistic knowledge is captured in constructions. These constructions pair form and meaning at varying levels of abstraction, ranging from purely substantive to fully abstract and are all acquired through situated communicative interactions. In this paper we provide computational support for these foundational principles. We present a model that enables an artificial learner agent to acquire a construction grammar directly from its sensory experience. The grammar is built from the ground up, i.e. without a given lexicon, predefined categories or ontology and covers a range of constructions, spanning from purely substantive to partially schematic. Our approach integrates two previously separate but related experiments, allowing the learner to incrementally build a linguistic inventory that solves a question-answering task in a synthetic environment. These findings demonstrate that linguistic knowledge at different levels can be mechanistically acquired from experience.

This paper presents the Candide model as a computational architecture for modelling human-like, narrative-based language understanding. The model starts from the idea that narratives emerge through the process of interpreting novel linguistic observations, such as utterances, paragraphs and texts, with respect to previously acquired knowledge and beliefs. Narratives are personal, as they are rooted in past experiences, and constitute perspectives on the world that might motivate different interpretations of the same observations. Concretely, the Candide model operationalises this idea by dynamically modelling the belief systems and background knowledge of individual agents, updating these as new linguistic observations come in, and exposing them to a logic reasoning engine that reveals the possible sources of divergent interpretations. Apart from introducing the foundational ideas, we also present a proof-of-concept implementation that demonstrates the approach through a number of illustrative examples.