Stochastic phonological grammars and acceptability

TL;DR

This paper uses a probabilistic phonological parser to model human judgments of nonsense word acceptability, revealing limitations in classical theories and proposing probabilistic grammars as more psychologically plausible.

Contribution

It introduces a probabilistic approach to phonological acceptability, highlighting the importance of part frequency and challenging traditional generative phonology and Optimality Theory.

Findings

Probabilistic scores better predict acceptability than worst part probability.

Classical theories overlook the role of part frequency in acceptability.

Probabilistic grammars align more closely with human phonological competence.

Abstract

In foundational works of generative phonology it is claimed that subjects can reliably discriminate between possible but non-occurring words and words that could not be English. In this paper we examine the use of a probabilistic phonological parser for words to model experimentally-obtained judgements of the acceptability of a set of nonsense words. We compared various methods of scoring the goodness of the parse as a predictor of acceptability. We found that the probability of the worst part is not the best score of acceptability, indicating that classical generative phonology and Optimality Theory miss an important fact, as these approaches do not recognise a mechanism by which the frequency of well-formed parts may ameliorate the unacceptability of low-frequency parts. We argue that probabilistic generative grammars are demonstrably a more psychologically realistic model of phonological competence than standard generative phonology or Optimality Theory.