Fluid Construction Grammar (FCG) is a computational framework that provides a formalism for representing construction grammars and a processing engine that supports construction- based language comprehension and production. FCG is conceived as a computational operationalisation of the basic tenets of construction grammar. It thereby aims to establish more solid foundations for constructionist theories of language, while expanding their application potential in the fields of artificial intelligence and natural language understanding. This paper aims to provide a brief introduction to the FCG research programme, reflecting on what has been achieved so far and identifying key challenges for the future.

One of AI’s grand challenges consists in the development of autonomous agents with communication systems offering the robustness, flexibility and adaptivity found in human languages. While the processes through which children acquire language are by now relatively well understood, a faithful computational operationalisation of the underlying mechanisms is still lacking. Two main cognitive processes are involved in child language acquisition. First, children need to reconstruct the intended meaning of observed utterances, a process called intention reading. Then, they can gradually abstract away from concrete utterances in a process called pattern finding and acquire productive schemata that generalise over form and meaning. In this paper, we introduce a mechanistic model of the intention reading process and its integration with pattern finding capacities. Concretely, we present an agent-based simulation in which an agent learns a grammar that enables them to ask and answer questions about a scene. This involves the reconstruction of queries that correspond to observed questions based on the answer and scene alone, and the generalization of linguistic schemata based on these reconstructed question-query pairs. The result is a productive grammar which can be used to map between natural language questions and queries without ever having observed the queries.

Despite the growing number of Computational Construction Grammar implementations, the field is still lacking evaluation methods to compare grammar fragments across different platforms. Moreover, the hand-crafted nature of most grammars requires profiling tools to understand the complex interactions between constructions of different types. This paper presents a number of evaluation measures, partially based on existing measures in the field of semantic parsing, that are especially relevant for reversible grammar formalisms. The measures are tested on a grammar fragment for European Portuguese clitic placement that is currently under development.