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Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge

Journal article
Authors Jey Han Lau
Alexander Clark
Shalom Lappin
Published in Cognitive Science
Volume Epub ahead of print
ISSN 0364-0213
Publication year 2016
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links dx.doi.org/10.1111/cogs.12414
https://gup-server.ub.gu.se/v1/asse...
Keywords Grammaticality; Syntactic knowledge; Probabilistic modeling
Subject categories Computer and Information Science, Psychology

Abstract

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large-scale experiments using crowd-sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state-of-the-art machine learning methods in natural language processing. Several of these models achieve very encourag- ing levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic. Keywords: Grammaticality; Syntactic knowledge; Probabilistic modeling

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