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 in this paper constitute a crucial contribution towards the development of scalable constructional language processing systems, thereby overcoming the major efficiency obstacle that hinders current efforts in learning large-scale construction grammars.
Original languageEnglish
Pages (from-to)287-314
JournalJournal of Language Modelling
Volume10
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

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

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