Neural Heuristics for Scaling Constructional Language Processing

Paul Van Eecke, Jens Nevens, Katrien Beuls

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Abstract

Constructionist approaches to language make use of form-meaning pairings, called constructions, to capture all linguistic knowledge that is necessary for comprehending and producing natural language expressions. Language processing consists then in combining the constructions of a grammar in such a way that they solve a given language comprehension or production problem. Finding such an adequate sequence of constructions constitutes a search problem that is combinatorial in nature and becomes intractable as grammars increase in size. In this paper, we introduce a neural methodology for learning heuristics that substantially optimise the search processes involved in constructional language processing. We validate the methodology in a case study for the CLEVR benchmark dataset. We show that our novel methodology outperforms state-of-the-art techniques in terms of size of the search space and time of computation, most markedly in the production direction. The results reported on in this paper have the potential to overcome the major efficiency obstacle that hinders current efforts in learning large-scale construction grammars, thereby contributing to the development of scalable constructional language processing systems.
Original languageEnglish
Pages (from-to)287-314
Number of pages27
JournalJournal of Language Modelling
Volume10
Issue number2
DOIs
Publication statusPublished - 28 Dec 2022

Keywords

  • neural heuristics
  • Fluid Construction Grammar
  • construction grammar
  • language processing

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