Generative Grammar, Neural Networks, and the Implementational Mapping Problem

Response to Pater

Bibliographic Data

ID22863583
AuthorsEwan Dunbar (0000-0001-9603-953X, Délégation Paris 7, corresponding author)
Year2019
Volume95
Issue1
Pagese87-e98
Publication date2019-03-01
Open AccessYes
TypeARTICLE
VenueLanguage (JOURNAL)
Journal identifiersISSN: 0097-8507 • E-ISSN: 1535-0665
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1353/lan.2019.0013
OpenAlexW2917613320
LanguageEN
Citations received3
References cited7

The target article (Pater 2019) proposes to use neural networks to model learning within existing grammatical frameworks. This is easier said than done. There is a fundamental gap to be bridged that does not receive attention in the article: how can we use neural networks to examine whether it is possible to learn some linguistic representation (a tree, for example) when, after learning is finished, we cannot even tell if this is the type of representation that has been learned (all we see is a sequence of numbers)? Drawing a correspondence between an abstract linguistic representational system and an opaque parameter vector that can (or perhaps cannot) be seen as an instance of such a representation is an implementational mapping problem. Rather than relying on existing frameworks that propose partial solutions to this problem, such as harmonic grammar, I suggest that fusional research of the kind proposed needs to directly address how to ‘find’ linguistic representations in neural network representations.

Artificial neural network · Generative grammar · Grammar · Linguistics · Natural language processing · Representation (politics) · Tree (set theory) · Artificial Intelligence · Computer Science · Mathematics · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism · Syntax, Semantics, Linguistic Variation

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