Dupre, G (2021) (What) Can Deep Learning Contribute to Theoretical Linguistics? Minds and Machines. ISSN 0924-6495

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Deep learning (DL) techniques have revolutionised artificial systems’ performance on myriad tasks, from playing Go to medical diagnosis. Recent developments have extended such successes to natural language processing, an area once deemed beyond such systems’ reach. Despite their different goals (technological development vs. theoretical insight), these successes have suggested that such systems may be pertinent to theoretical linguistics. The competence/performance distinction presents a fundamental barrier to such inferences. While DL systems are trained on linguistic performance, linguistic theories are aimed at competence. Such a barrier has traditionally been sidestepped by assuming a fairly close correspondence: performance as competence plus noise. I argue this assumption is unmotivated. Competence and performance can differ arbitrarily. Thus, we should not expect DL models to illuminate linguistic theory.

Item Type: Article
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Subjects: H Social Sciences > H Social Sciences (General)
T Technology > T Technology (General)
Divisions: Faculty of Humanities and Social Sciences > School of Social, Political and Global Studies
Depositing User: Symplectic
Date Deposited: 30 Nov 2021 16:03
Last Modified: 30 Nov 2021 16:03
URI: https://eprints.keele.ac.uk/id/eprint/10325

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