Modelling Language Acquisition through Syntactico-Semantic Pattern Finding

Jonas Doumen, Katrien Beuls, Paul Van Eecke

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Abstract

Usage-based theories of language acquisition have extensively documented the processes by which children acquire language through communicative interaction. Notably, Tomasello (2003) distinguishes two main cognitive capacities that underlie human language acquisition: intention reading and pattern finding. Intention reading is the process by which children try to continuously reconstruct the intended meaning of their interlocutors. Pattern finding refers to the process that allows them to distil linguistic schemata from multiple communicative interactions. Even though the fields of cognitive science and psycholinguistics have studied these processes in depth, no faithful computational operationalisations of these mechanisms through which children learn language exist to date. The research on which we report in this paper aims to fill part of this void by introducing a computational operationalisation of syntactico-semantic pattern finding. Concretely, we present a methodology for learning grammars based on similarities and differences in the form and meaning of linguistic observations alone. Our methodology is able to learn compositional lexical and item-based constructions of variable extent and degree of abstraction, along with a network of emergent syntactic categories. We evaluate our methodology on the CLEVR benchmark dataset and show that the methodology allows for fast, incremental and effective learning. The constructions and categorial network that result from the learning process are fully transparent and bidirectional, facilitating both language comprehension and production. Theoretically, our model provides computational evidence for the learnability of usage-based constructionist theories of language acquisition. Practically, the techniques that we present facilitate the learning of computationally tractable, usage-based construction grammars, which are applicable for natural language understanding and production tasks.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: EACL 2023
PublisherAssociation for Computational Linguistics
Pages1347–1357
Number of pages11
ISBN (Electronic)9781959429470
Publication statusPublished - 2023
EventThe 17th Conference of the European Chapter of the Association for Computational Linguistics - Dubrovnik, Croatia
Duration: 2 May 20234 May 2023
Conference number: 17
https://2023.eacl.org

Publication series

NameEACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023

Conference

ConferenceThe 17th Conference of the European Chapter of the Association for Computational Linguistics
Abbreviated titleEACL
Country/TerritoryCroatia
CityDubrovnik
Period2/05/234/05/23
Internet address

Bibliographical note

Funding Information:
The research reported on in this paper received funding from the imec’s Smart Education research programme, with support from the Flemish government, the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 951846, and the Research Foundation Flanders (FWO) through a postdoctoral grant awarded to Paul Van Eecke (75929).

Publisher Copyright:
© 2023 Association for Computational Linguistics.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Keywords

  • pattern finding
  • construction grammar
  • Fluid Construction Grammar
  • language acquisition
  • computational linguistics

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