Linguistic Optimization

Optimality Theory (OT) is a model of language that combines aspects of generative and connectionist linguistics. It is unique in the field in its use of a rank ordering on constraints, which is used to formalize optimization, the choice of the best of a set of potential linguistic forms. We show that phenomena argued to require ranking fall out equally from the form of optimization in OT's predecessor Harmonic Grammar (HG), which uses numerical weights to encode the relative strength of constraints. We further argue that the known problems for HG can be resolved by adopting assumptions about the nature of constraints that have precedents both in OT and elsewhere in computational and generative linguistics. This leads to a formal proof that if the range of each constraint is a bounded number of violations, HG generates a finite number of languages. This is nontrivial, since the set of possible weights for each constraint is nondenumerably infinite. We also briefly review some advantages of HG

Weighted Constraints in Generative Linguistics

Harmonic Grammar (HG) and Optimality Theory (OT) are closely related formal frameworks for the study of language. In both, the structure of a given language is determined by the relative strengths of a set of constraints. They differ in how these strengths are represented: as numerical weights (HG) or as ranks (OT). Weighted constraints have advantages for the construction of accounts of language learning and other cognitive processes, partly because they allow for the adaptation of connectionist and statistical models. HG has been little studied in generative linguistics, however, largely due to influential claims that weighted constraints make incorrect predictions about the typology of natural languages, predictions that are not shared by the more popular OT. This paper makes the case that HG is in fact a promising framework for typological research, and reviews and extends the existing arguments for weighted over ranked constraints

Typological gaps in iambic nonfinality correlate with learning difficulty

This paper discusses gaps in stress typology that are unexpected from the perspective of a foot-based theory and shows that the patterns pose difficulties for a computationally implemented learning algorithm. The unattested patterns result from combining theoretical elements whose effects are generally well-attested, including iambic footing, nonfinality, word edge alignment and a foot binarity requirement. The patterns can be found amongst the 124 target stress systems constructed by Tesar and Smolensky (2000) as a test of their approach to hidden structure learning. A learner with a Maximum Entropy grammar that uses a form of Expectation Maximization to deal with hidden structure was found to often fail on these unattested languages

Learning Stress Patterns with a Sequence-to-Sequence Neural Network

We present the first application of modern neural networks to the well studied task of learning word stress systems. We tested our adaptation of a sequence-to-sequence network on the Tesar and Smolensky test set of 124 languages , showing that it acquires generalizable representations of stress patterns in a very high proportion of runs. We also show that it learns restricted lexically conditioned patterns, known as stress windows. The ability of this model to acquire lexical idiosyncracies, which are very common in natural language systems, sets it apart from past, non-neural models tested on the Tesar and Smolensky data set

Slightly revised version to appear in Second

Abstract This paper presents a follow-up to Curtin et. al's study of the perceptual acquisition of Thai laryngeal contrasts by native speakers of English, which found that subjects performed better on contrasts in voice than aspiration. This finding, surprising in light of earlier cross-linguistic VOT research, was attributed to the fact that the task tapped lexical representations, which are unspecified for aspiration, according to standard assumptions in generative phonology. The present study further investigated possible task effects by examining the discrimination and categorization of the same stimuli in various experimental conditions. Stimulus effects were also investigated by performing token-based analyses of the results, and by comparing them to acoustic properties of the tokens. The outcome of the discrimination experiment was the opposite of the earlier study, with significantly better performance on contrasts in aspiration than voice, even on a lexical task. A second finding of this experiment is that place of articulation interacts with the perception of the laryngeal distinctions; the aspiration distinction is discriminated better on the labials, and voice on alveolars. A parallel effect of place of articulation was also found in a categorization experiment

Learning Stress with Feet and Grids

This paper investigates quantity-insensitive stress learning using the MaxEnt learner of Pater and Prickett (2022) and compares the performance of the learner equipped with three different constraint sets: a foot-based constraint set and two grid-based constraint sets, one drawn directly from Gordon (2002), and one that changes the formulation of the main stress constraint to match the foot-based learner. The learner equipped with the foot-based constraint set succeeds at learning all the languages from the Gordon (2002) typology that it can represent; the structural ambiguity of the foot-based representations is not a problem in this regard. The foot-based learner also learns the languages as quickly in terms of number of epochs as the faster of the grid-based learners, which is the one with the revised main stress constraint. We conclude that the foot-based learner and the grid-based learner fare similarly well in this initial comparison on a typologically grounded set of learning problems