Categorical Account of Gradient Acceptability of Word-Initial Polish Onsets

Abstract

We examine how well categorical and probabilistic phonotactic learning models extract grammars which predict Polish speakers' acceptability judgments of words with varied initial consonant clusters. Polish is an especially interesting language to look at because of its rich inventory of sonority-sequencing defying consonant clusters, often as a result of yer-deletion. In line with results by Gorman (2013) and Durvasula (2020), we find that the categorical baselines considered here generally outperformed the Hayes and Wilson's (2008) maximum-entropy based phonotactic learner. We conclude that gradient acceptability judgments do not provide unambiguous evidence for gradient, probabilistic grammars.

Categorical versus gradient grammar in phonotactics

“…Recent studies have provided accounts of gradient acceptability in phonotactics by employing very simple categorical grammars, matching or outperforming the linear correlation levels of well-known probabilistic grammars with acceptability data (see Durvasula, 2020;Gorman, 2013). In the most recent experiment with Polish phonotactics (Kostyszyn & Heinz, 2022), the authors compared two categorical models (Cluster and Stricly 2-Local) with the MaxEnt model of Hayes and Wilson (2008). The Cluster model assigns score 0 to a (nonce) word if its onset is found in the training data; otherwise the score is 1.…”