Fluid Construction Grammar: State of the Art and Future Outlook (2023)

Abstract: 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.

## introduction fluid construction grammar (fcg 1 ) @xcite @xcite is a computational framework that aims to operationalise the foundational principles underlying constructionist approaches to language. on a high level, the fcg framework serves two main purposes. on the one hand, it aims to provide a solid methodological basis for studying the emergence, evolution, acquisition and processing of language from a construction grammar perspective, through a standardised formalisation and a tractable computational operationalisation. on the other hand, it aims to facilitate the building of intelligent agents that are capable of communicating with humans and each other through languages that exhibit the robustness, flexibility and adaptivity of human languages. in this paper, we aim to provide a brief introduction to the fcg research programme, highlighting its relevance in the field of linguistics on the one hand, and in the fields of artificial intelligence and natural language understanding on the other. we start by situating the fcg framework within the field of construction grammar (section 2) and then lay out in a step-by-step manner how fcg provides a faithful computational operationalisation of the basic tenets of construction grammar (section 3). we then discuss how constructions are learned as compositional generalisations over recurring syntactico-semantic patterns (section 4) and proceed with an overview of applications that integrate fcg technologies (section 5). finally, we consider a number of key challenges and opportunities for future computational construction grammar research and conclude that the automatic learning of large-scale, usage-based construction grammars that support both language comprehension and production is a promising and timely research direction that is now well within reach (section 6). ## situating fluid construction grammar over the last four decades, the linguistic community has become increasingly more interested in constructionist approaches to language, as witnessed by the increased presence of talks, tutorials, courses and workshops at international conferences and schools @xcite . the term 'constructionist approaches to language' @xcite is used to refer to a variety of theoretical frameworks, which all share a number of key foundational principles. these principles, as laid out by among others @xcite , @xcite , @xcite and @xcite , and which are commonly referred to as the basic tenets of construction grammar, are summarised by @xcite as follows: the same formal machinery is used to handle all phenomena. constructionist theories of language have explicitly or implicitly built on these basic tenets since the 1980s, with initial formalisations being inspired by phrase structure grammars @xcite . later, when the lakovian/goldbergian branch of construction grammar, often referred to as cognitive construction grammar, became predominant @xcite , the focus on formalisation gradually faded into the background. as is justifiable for an emerging field of research, the focus was more on the conceptual clarification of the loosely defined innovative ideas, rather than on the construction of a solid methodological framework (cf. @xcite . however, the absence of such a framework led to the criticism that construction grammar was often not more than "a set of insightful but untestable ideas" @xcite . initial efforts to establish such a framework in the early 2000s gave rise to the emergence of the field of computational construction grammar, with embodied construction grammar (ecg) @xcite , sign-based construction grammar (sbcg) @xcite and fluid construction grammar @xcite @xcite being the most advanced projects in this area. computational operationalisations of construction grammar have four main objectives. first of all, they are important for verifying the internal consistency of construction grammar theories, which is impossible to do by hand for larger grammars. second, they facilitate the large-scale empirical validation of these theories on corpora of language use. third, they can serve as a standard for exchange and collaboration between construction grammar researchers. finally, they make it possible to exploit construction grammar insights and analyses for the purpose of building language technology applications. ## operationalising construction grammar we will now briefly discuss how the basic tenets of construction grammar can be computationally operationalised. we will focus solely on how this is achieved in the framework of fluid construction grammar. for an introduction into the fcg system itself, including the syntax and semantics of the formalism, we refer the reader to chapter 3 of van eecke (2018). ## learning construction grammars now that we have established computational representations for constructions, as well as processing mechanisms that use these constructions for operationalising language comprehension and production, we can approach the question of where these constructions originate and how they are shaped by the communicative needs of their hosts. again, we start from theoretical and empirical work in usagebased linguistics with the aim of building mechanistic models that computationally operationalise the theoretical insights that were obtained and the empirical evidence that was gathered, in order to support communication in artificial agents. usage-based theories of language acquisition describe two main cognitive processes involved in the acquisition of language through communicative interactions: intention reading and pattern finding @xcite . intention reading is the process through which listeners hypothesize about the intended meaning of an observed utterance, by reasoning about the situation in which this utterance was formulated. for example, when a child observes the utterance "more-milk?" and receives more milk at the same time, the child can hypothesise that the meaning of this utterance is that an additional portion of this thirst-quenching white liquid will be served. pattern finding is then the generalisation process through which abstract patterns can be distilled. for example, if the same child then observes the utterance "more-mash?" in a situation where it is served an additional portion of this delicious soft mass, it can generalise to the pattern "more-x?" with the meaning of being served an additional portion of something. at the same time, it can infer that "milk" and "mash" respectively refer to this thirst-quenching white liquid and this delicious soft mass. the processes of intention reading and pattern finding yield pairings between meaning and form and therefore, by definition, constructions. initially, a child, or an intelligent agent in our case, cannot do more than store holistic mappings between observed utterances and their (hypothesised) meanings. indeed, these holophrastic constructions are at this point not further decomposable, as the learner has no information on whether and how specific parts of their meaning might correspond to specific parts of their form. later, when similar, yet not identical utterances are observed in similar, yet not identical situations, more general itembased constructions can be created, through generalisation over the compositional parts of the formmeaning pairings. the item-based constructions then capture the relations between these variable elements along with any non-compositional aspects of the original form-meaning mappings. over the course of increasingly more communicative interactions, these generalisations lead to increasingly more abstract constructions. at some point, the constructions adequately reflect the compositional and non-compositional aspects of the language, and the construction inventory of the learner stabilises. as intention reading hypothesises about the intended meaning underlying an observed utterance in a particular situation, it can yield a hypothesis that might hold in that situation but not in others. consequently, generalisation over these suboptimal hypotheses might lead to more abstract constructions that are suboptimal as well. it is here that the entrenchment dynamics described in the previous section come into play. over time, constructions that are used more frequently and successfully in communication become more entrenched. at the same time, suboptimal constructions, which often lead to communicative failure, become less entrenched until they eventually disappear from the construction inventory of the learner. after a sufficient number of communicative interactions, the construction inventory stabilises on a set of generally applicable constructions that cover the learner's communicative needs. the process of language acquisition through intention reading and pattern finding is operationalised in fluid construction grammar through a meta-layer learning architecture that supports (i) the composition of meaning representations based on the situation (intention reading) and (ii) the generalisation over form-meaning mappings of various degrees of abstraction (pattern finding) @xcite @xcite . an example of such a generalisation operation, taken from the experiment described in @xcite is shown in figure 2 . in this figure, a learner agent observes the utterance "how many cylinders are there?" in a 3d scene of geometrical figures, but cannot understand it, as the utterance is currently not covered by the constructions in its construction inventory. the learner agent receives feedback in the form of the answer to the question ("one"). starting from this answer, it can then construct a meaning hypothesis. in this case, the agent hypothesises that the utterance "how many cylinders are there?" corresponds to the meaning of segmenting the scene, activating the cylinder prototype, using that prototype to filter the segmented scene for cylinders and counting the items in the filtered set. indeed, upon execution, this procedural semantic representation leads to the answer "one" in this scene. the result of the intention reading operation is shown in subfigure a of figure 2 . the agent then identifies a construction in its construction inventory that is minimally different from this pairing between the observed utterance and its hypothesised meaning, namely the holophrastic how-many-spheres-are-there-?-cxn shown in subfigure b. this previously acquired construction maps between the utterance "how many spheres are there?" and the meaning of segmenting the scene, activating the sphere prototype, using that prototype to filter the segmented scene for spheres and counting the items in the filtered set. based on this previously acquired construction, the observed utterance and its hypothesised meaning, the agent creates a new item-based "how many cylinders are there?" figure 3 , adopted from nevens et al. ( 2022 ), shows the typical dynamics of an experiment in which a construction grammar is learnt through intention reading and pattern finding. the x-axis corresponds to the time dimension, expressed here in terms of the number of communicative interactions in which an agent has participated. the y-axis shows the average communicative success (green line) and the number of construction in the construction inventory of the agent (yellow line) over time. the communicative success starts at 0, as the agent starts without any constructions in its construction inventory. as more and more interactions take place, the communicative success rises to 1, which means that every communicative interaction is successful. the number of constructions in the construction inventory of the agent starts at 0 as well, and then rapidly grows to over 1000. it then starts to decrease, as a result of the entrenchment dynamics, and stabilises somewhere between 150 and 200. during this process, constructions capturing communicatively inadequate form-meaning mappings, as well as form-meaning mappings that are also captured by more generally applicable constructions, disappear from the grammar. the example discussed in this section is meant to illustrate how an inventory of constructions can be learnt in a usage-based fashion through syntacticosemantic generalisation operations, and how the construction inventory of an agent is shaped by past successes and failure in communication. the exact mechanisms through which the intention reading and pattern finding processes are operationalised, along with the a precise definition of the entrenchment dynamics, fall outside the scope of this paper, although we happily refer the interested reader to publications such as @xcite and @xcite . ## applications of fcg fluid construction grammar was originally developed to be used as the language processing component in experiments on the emergence and evolution of language (see e.g. @xcite @xcite @xcite @xcite . as such, it is designed to represent the emergent linguistic knowledge of autonomous agents, as well as to use this knowledge for language comprehension and production. the fact that fcg is rooted in such experiments is reflected in a number of important design choices. first of all, fcg focusses on representations of linguistic knowledge that are adequate for both language comprehension and production. second, it provides good support for grounded language processing, for example by providing the possibility to use procedural semantic representations @xcite @xcite and procedural attachment in the constructions @xcite . third, it focusses on the data-efficient learning of constructions, whereby as much linguistic knowledge as possible is extracted from individual communicative interactions. fourth, it uses transparent and human-interpretable representations. finally, it is designed to represent and process ever-evolving grammars, in which new constructions can dynamically be added and in which adequacy of constructions can evolve as changes in the environment or task take place. while experiments on the emergence and evolution of languages in populations of autonomous agents through task-based communicative interactions are the most prominent application domain for fcg, the design properties mentioned above also make it an attractive framework for a wider range of applications. a first series of applications tackles typical nlp/nlu benchmarks that focus on grounded language processing on the one hand, and on the integration of language processing and reasoning on the other. typical examples of such tasks are visual question answering (vqa) and visual dialogue, in which the task consists in answering a series of questions about a given image. fcg is then used as a semantic parsing module, which maps from questions to executable queries @xcite . the main advantage of the use of fcg in such applications, as compared to the use of neural approaches, is that it provides a transparent and explainable model that can be learnt efficiently @xcite . interactive demonstrations of the use of fcg in nlp/nlu systems are provided at the following links respectively for vqa foot_0 and visual dialogue 3 . a second series of applications makes use of fcg to support the analysis of opinion dynamics expressed in online (social) media. in particular, fcg is used in such applications to extract semantic frames from textual data, such as newspaper articles and comments, subreddits, and parliamentary speeches. concrete examples are the penelope climate change opinion observatory foot_2 and the penelope opinion facilitator foot_3 . the opinion observatory @xcite aims to provide social science researchers with a low-barrier tool for studying opinion landscapes expressed in a wide range of digital sources. the opinion facilitator @xcite aims to provide a reading instrument for the general public that automatically interlinks news articles based on the expression of similar or opposing views. both tools focus on opinions expressed in the context of the climate change debate and thereby emphasise the detection and extraction of causal frames @xcite . a final series of applications makes use of fcg to support linguistic research. apart from the obvious advantages that a computational construction grammar implementation brings to the construction grammarian, including the automatic verification and empirical validation of construction grammars, fcg can also serve as the basis for methodological tools supporting usage-based linguistic research. an example of such a tool is the ccxg explorer foot_4 , which enables usage-based linguists to search for corpus examples that instantiate a semantic structure of interest using any morpho-syntactic realisation. in this way, they can find examples of morpho-syntactic phenomena without the need to identify these phenomena beforehand as is required with other tools. ## conclusion and outlook the primary objective of this paper was to provide a brief introduction to the fluid construction grammar research programme, reflecting on what has been achieved so far and identifying key challenges for the future. let us start by reflecting on the achievements. first of all, we now have at our disposal a computational framework that provides a faithful formalisation and computational operationalisation of the basic tenets of construction grammar. this framework can be used to represent linguistic knowledge in the form of constructions and to use these constructions for language comprehension and production purposes. we also have a basic theory of how constructions can be acquired in a usage-based fashion through syntacticosemantic generalisation processes. finally, the application potential of fcg has been demonstrated extensively in experiments on emergent languages and on a smaller scale in a number of proof-ofconcept language technology applications. while the last decade has undeniably witnessed major advances in the fcg framework and research programme, even more fascinating challenges and exciting opportunities lie ahead of us now. a first challenge concerns the scaling of constructionist approaches to language on both the theoretical and the computational level, in particular when it comes to modelling the systemic relations between hundreds of thousands of constructions. a second challenge concerns the further development of syntactico-semantic learning operators. this includes for example the design of pattern finding operators that can more elegantly handle linguistic phenomena related to grammatical agreement, more general algorithms for generalising over semantic structures, and techniques that can find minimal differences between speech signals rather than strings. a third challenge resides in converting the recent advances achieved in the domain of learning large-scale fcg grammars into powerful language technology applications. a final challenge concerns the abstraction of fcg's learning mechanisms into an accessible toolbox for end-users. this toolbox would enable ai and nlp engineers to equip intelligent agents with the ability to acquire communicatively adequate grammars during situated task-oriented interactions. in sum, we strongly believe that the future of computational construction grammar looks brighter than ever and that hugely exciting times lie ahead.