Differentiable Generative Phonology

TL;DR

This paper introduces a neural, differentiable model of generative phonology that automatically learns underlying forms as continuous vectors, scaling to large vocabularies and providing insights into phonological theory.

Contribution

It implements a neural end-to-end differentiable system for generative phonology, replacing traditional rule-based models with continuous representations of underlying forms.

Findings

UFs are essential for accurate phonological string prediction.

UFs are independent of surface forms, supporting traditional phonological assumptions.

The model scales to large datasets across multiple languages.

Abstract

The goal of generative phonology, as formulated by Chomsky and Halle (1968), is to specify a formal system that explains the set of attested phonological strings in a language. Traditionally, a collection of rules (or constraints, in the case of optimality theory) and underlying forms (UF) are posited to work in tandem to generate phonological strings. However, the degree of abstraction of UFs with respect to their concrete realizations is contentious. As the main contribution of our work, we implement the phonological generative system as a neural model differentiable end-to-end, rather than as a set of rules or constraints. Contrary to traditional phonology, in our model, UFs are continuous vectors in $\mathbb{R}^d$, rather than discrete strings. As a consequence, UFs are discovered automatically rather than posited by linguists, and the model can scale to the size of a realistic vocabulary. Moreover, we compare several modes of the generative process, contemplating: i) the presence or absence of an underlying representation in between morphemes and surface forms (SFs); and ii) the conditional dependence or independence of UFs with respect to SFs. We evaluate the ability of each mode to predict attested phonological strings on 2 datasets covering 5 and 28 languages, respectively. The results corroborate two tenets of generative phonology, viz. the necessity for UFs and their independence from SFs. In general, our neural model of generative phonology learns both UFs and SFs automatically and on a large-scale.