(What) Can Deep Learning Contribute to Theoretical Linguistics?

Dupre

doi:10.1007/s11023-021-09571-w

(What) Can Deep Learning Contribute to Theoretical Linguistics?

Dupre

Authors

Dupre

Abstract

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.

Acceptance Date	Aug 24, 2021
Publication Date	Sep 10, 2021
Journal	Minds and Machines
Print ISSN	0924-6495
Publisher	Springer Verlag
DOI	https://doi.org/10.1007/s11023-021-09571-w
Publisher URL	https://link.springer.com/article/10.1007%2Fs11023-021-09571-w