Stochastic phonological grammars and acceptability

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.Comment: compressed postscript, 8 pages, 1 figur

Learning OT constraint rankings using a maximum entropy model

Abstract. A weakness of standard Optimality Theory is its inability to account for grammar

Degraded acceptability and markedness in syntax, and the stochastic interpretation of optimality theory

The argument that I tried to elaborate on in this paper is that the conceptual problem behind the traditional competence/performance distinction does not go away, even if we abandon its original Chomskyan formulation. It returns as the question about the relation between the model of the grammar and the results of empirical investigations – the question of empirical verification The theoretical concept of markedness is argued to be an ideal correlate of gradience. Optimality Theory, being based on markedness, is a promising framework for the task of bridging the gap between model and empirical world. However, this task not only requires a model of grammar, but also a theory of the methods that are chosen in empirical investigations and how their results are interpreted, and a theory of how to derive predictions for these particular empirical investigations from the model. Stochastic Optimality Theory is one possible formulation of a proposal that derives empirical predictions from an OT model. However, I hope to have shown that it is not enough to take frequency distributions and relative acceptabilities at face value, and simply construe some Stochastic OT model that fits the facts. These facts first of all need to be interpreted, and those factors that the grammar has to account for must be sorted out from those about which grammar should have nothing to say. This task, to my mind, is more complicated than the picture that a simplistic application of (not only) Stochastic OT might draw

Examining variability in Spanish monolingual and bilingual phonotactics: A look at sC-clusters

Current models of generative phonology have failed to address the variability that is observed in bilingual language patterns patterns. This dissertation addresses exactly that issue by examining the perception of Spanish sC-clusters in Spanish monolinguals and English-Spanish bilinguals. Surface sC-clusters in onset position are prohibited in Spanish and are repaired by inserting a prothetic /e/ (sC $\rightarrow$ esC). English differs in that it allows sC-cluster onsets, and the structure of the sC-cluster has been shown to differ based on the sonority profile (i.e., s+stop clusters are bisyllabic, s+liquid clusters are tautosyllabic). A batch version of a Harmonic Grammar Gradual Learning Algorithm (HG-GLA) was given Spanish input and predicted that Spanish sC-clusters may be syllabified differently based on the sonority of the sC-cluster. It predicted that s+stop clusters are more likely to instantiate /e/ prothesis than s+liqud clusters, but that s+liquid clusters are most likely to be syllabified as a true branching onset like in English. This led to the hypothesis that s+stop and s+liquid clusters may show observable differences in perception in Spanish. Furthermore, studies in bilingualism have shown strong evidence for bilingual variability, or non-monolingual-like language behavior, particularly in areas where there is non-identical structural overlap, as is the case with sC-clusters in Spanish and English. The perception of s+stop and s+liquid clusters was thus also analyzed with respect to the following language-external variables that affect bilingual variability: language profile (monolingual versus bilingual), age of exposure to bilingualism, and bilingual dominance. To test these hypotheses, two experiments were performed. The first was a replication of an AX task that has been shown to exhibit variability in Spanish sC-cluster perception in past studies. In this task, native Spanish speakers (monolingual and bilingual) listened to stimuli pairs that differed in the duration and quality of the initial vowel preceding the sC-cluster and were asked to respond if they were the same or different. The second was a nonce word judgment task where participants were presented with Spanish-like nonce words beginning with sC-clusters and had to give them acceptability ratings of how `Spanish-like\u27 they sounded. The results did not show evidence of a language-internal effect. s+stop and s+liquid clusters were treated the same in perception by Spanish native speakers, contrary to the predictions of the HG-GLA. Regarding the language-external variables, there was a strong effect of language profile on perception of sC-clusters in Spanish: monolinguals showed a strong dis-preference for sC-initial words, whereas bilinguals were more accepting of such clusters. However, the bilingual variability observed was not affected by age of exposure to bilingualism or by language dominance. Finally, a sketch of a proposal is made for how generative theories of phonology, like Harmonic Grammar, could potentially be adapted to accommodate the observed differences between the phonotactics of monolinguals and bilinguals, particularly for the case of sC-clusters in English-Spanish bilinguals

Learning Nonlocal Phonotactics in a Strictly Piecewise Probabilistic Phonotactic Model

Phonotactic learning is a crucial aspect of phonological acquisition and has figured significantly in computational research in phonology (Prince & Tesar 2004). However, one persistent challenge for this line of research is inducing non-local co-occurrence patterns (Hayes & Wilson 2008). The current study develops a probabilistic phonotactic model based on the Strictly Piecewise class of subregular languages (Heinz 2010). The model successfully learns both segmental and featural representations, and correctly predicts the acceptabilities of the nonce forms in Quechua (Gouskova & Gallagher 2020)

Against the Law of Three Consonants in French: Evidence from Judgment Data

Grammont's Law of Three Consonants (LTC) states that French schwa is obligatorily pronounced in any CC_C sequence to avoid three-consonant clusters. Although schwa presence has been shown to be sensitive not only to cluster size but also to the nature of consonants in post-lexical phonology, the LTC is still considered as accurate to describe schwa-zero alternations in lexical phonology. The paper uses judgment data from French speakers in France and Switzerland to compare the behavior of schwa in derived words (lexical phonology) and inflected words (post-lexical phonology). The results show that schwa-zero alternations are conditioned not only by cluster size but also by cluster type in lexical phonology. Moreover, the same phonotactic asymmetries among consonant clusters are found in lexical and post-lexical phonologies. The data therefore support a weaker version of the lexical-phonology hypothesis than what is usually assumed for French. Lexical and post-lexical phonologies do not require different phonotactic constraints but only different weights for the same constraints

Rethinking Representations: A Log-bilinear Model of Phonotactics

Models of phonotactics include subsegmental representations in order to generalize to unattested sequences. These representations can be encoded in at least two ways: as discrete, phonetically-based features, or as continuous, distribution-based representations induced from the statistical patterning of sounds. Because phonological theory typically assumes that representations are discrete, past work has reduced continuous representations to discrete ones, which eliminates potentially relevant information. In this paper we present a model of phonotactics that can use continuous representations directly, and show that this approach yields competitive performance on modeling experimental judgments of English sonority sequencing. The proposed model broadens the space of possible phonotactic models by removing requirements for discrete features, and is a step towards an integrated picture of phonotactic learning based on distributional statistics and continuous representations

Phonotactic learning with neural language models

Computational models of phonotactics share much in common with language models, which assign probabilities to sequences of words. While state of the art language models are implemented using neural networks, phonotactic models have not followed suit. We present several neural models of phonotactics, and show that they perform favorably when compared to existing models. In addition, they provide useful insights into the role of representations on phonotactic learning and generalization. This work provides a promising starting point for future modeling of human phonotactic knowledge

Learning Phonotactics in a Differentiable Framework of Subregular Languages

Phonotactic constraints have been argued to beregular, meaning that they can be represented usingfinite-state automata (Heinz, 2018); furthermore, they have been argued to occupy a even more restrictedregion of the regular language class known as the subregular hierarchy (Rogers & Pullum, 2011). Ourcontribution is to present a simple model of phonotactic learning from positive evidence. Our approach isbased on probabilistic finite-state automata (Vidal et al., 2005a,b). We study the model’s ability to induce localand nonlocal phonotactics from wordlist data, both with and without formal constraints on the automaton.In particular, we evaluate the ability of our learner to induce nonlocal phonotactic constraints from data ofNavajo and Quechua. Our work provides a framework in which different formal models of phonotactics canbe compared, and sheds light on the structural nature of phonological acquisition (Dai, 2021; Shibata & Heinz,2019; Heinz & Rogers, 2010, 2013)

OPTIMALITY THEORY IN LANGUAGE PRODUCTION: THE CHOICE BETWEEN DIRECT AND INVERSE IN ODAWA

This paper proposes an analysis of variability in sentence production in the 'nonconfigurational' Algonquian language Odawa. In doing so, the role played by various hierarchies at work in the language is demonstrated, and it is shown how these hierarchies interact to explain the frequencies with which certain constructions occur in various contexts. In doing so, a version of Optimality Theory is employed, which, although technically 'non-standard', is consistent with recent work on language variation and variation in the evaluation function of the theory. As a result, several issues-both empirical and theoretical-are raised for future research