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 encouraging 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. (shrink)

A robust result in research on the production of grammatical agreement is that speakers are more likely to produce an erroneous verb with phrases such as the key to the cabinets, with a singular noun followed by a plural one, than with phrases such as the keys to the cabinet, where a plural noun is followed by a singular. These asymmetries are thought to reflect core language production processes. Previous accounts have attributed error patterns to a syntactic number feature present (...) on plurals but not singulars. An alternative approach is presented in which a process similar to structural priming contributes to the error asymmetry via speakers' past experiences with related agreement constructions. A corpus analysis and two agreement production studies test this account. The results suggest that agreement production is shaped by statistical learning from past language experience. Implications for accounts of agreement are discussed. (shrink)