Abstractions made of exemplars or 'You're all right, and I've changed my mind': Response to commentators

Ambridge, Ben ORCID: 0000-0003-2389-8477 (2020) Abstractions made of exemplars or 'You're all right, and I've changed my mind': Response to commentators. FIRST LANGUAGE, 40 (5-6). pp. 640-659.

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Official URL: http://dx.doi.org/10.1177/0142723720949723

Abstract

<jats:p>In this response to commentators, I agree with those who suggested that the distinction between exemplar- and abstraction-based accounts is something of a false dichotomy and therefore move to an abstractions-made-of-exemplars account under which (a) we store all the exemplars that we hear (subject to attention, decay, interference, etc.) but (b) in the service of language use, re-represent these exemplars at multiple levels of abstraction, as simulated by computational neural-network models such as BERT, ELMo and GPT-3. Whilst I maintain that traditional linguistic abstractions (e.g. a DETERMINER category; SUBJECT VERB OBJECT word order) are no more than human-readable approximations of the type of abstractions formed by both human and artificial multiple-layer networks, I express hope that the abstractions-made-of-exemplars position can point the way towards a truce in the language acquisition wars: We were all right all along, just focusing on different levels of abstraction.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Abstractions, computational modelling, deep learning, exemplars, neural networks
Depositing User:	Symplectic Admin
Date Deposited:	07 Oct 2020 09:54
Last Modified:	18 Jan 2023 23:29
DOI:	10.1177/0142723720949723
Open Access URL:	https://doi.org/10.1177%2F0142723720949723
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3103822