In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in a multilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model outperforms the other two in terms of accuracy, but the Sigmoid model's language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research

The evolution of auditory contrast

This paper reconciles the standpoint that language users do not aim at improving their sound systems with the observation that languages seem to improve their sound systems. Computer simulations of inventories of sibilants show that Optimality-Theoretic learners who optimize their perception grammars automatically introduce a so-called prototype effect, i.e. the phenomenon that the learner’s preferred auditory realization of a certain phonological category is more peripheral than the average auditory realization of this category in her language environment. In production, however, this prototype effect is counteracted by an articulatory effect that limits the auditory form to something that is not too difficult to pronounce. If the prototype effect and the articulatory effect are of a different size, the learner must end up with an auditorily different sound system from that of her language environment. The computer simulations show that, independently of the initial auditory sound system, a stable equilibrium is reached within a small number of generations. In this stable state, the dispersion of the sibilants of the language strikes an optimal balance between articulatory ease and auditory contrast. The important point is that this is derived within a model without any goal-oriented elements such as dispersion constraints

A distributional semantic approach to the periodization of change in the productivity of constructions

Abstract This paper describes a method to automatically identify stages of language change in diachronic corpus data, combining variability-based neighbour clustering, which offers objective and reproducible criteria for periodization, and distributional semantics as a representation of lexical meaning. This method partitions the history of a grammatical construction according to qualitative stages of productivity corresponding to different semantic sets of lexical items attested in it. Two case studies are presented. The first case study on the hell-construction (“Verb the hell out of NP”) shows that the semantic development of a construction does not always match that of its quantitative aspects, like token or type frequency. The second case study on the way-construction compares the results of the present method with those of collostructional analysis. It is shown that the former measures semantic changes and their chronology with greater precision. In sum, this method offers a promising approach to exploring semantic variation in the lexical fillers of constructions and to modelling constructional change.</jats:p

The Phonological Influence on Phonetic Change

This dissertation addresses the broad question about how phonology and phonetics are interrelated, specifically how phonetic language changes, which gradually alter the phonetics of speech sounds, affect the phonological system of the language, and vice versa. Some questions I address are: (i) What aspects of speakers\u27 knowledge of their language are changing during a phonetic change? (ii) What is the relative timing of a phonetic change and phonological reanalysis? (iii) Can a modular feed-forward model of phonology and phonetics account of the observed patterns of phonetic change? (iv) What are the consequences of my results for theories of phonology, phonetics, and language acquisition? (v) What unique insight into the answers to these questions can the study of language change in progress give us over other methodologies? To address these questions, I drew data from the Philadelphia Neighborhood Corpus [PNC] (Labov and Rosenfelder, 2011), a collection of sociolinguistic interviews carried out between 1973 and 2013. Using the PNC data, I utilized a number of different statistical modeling techniques to evaluate models of phonetic change and phonologization, including standard mixed effects regression modeling in R (Bates, 2006), and hierarchical Bayesian modeling via Hamiltonian Monte Carlo in Stan (Stan Development Team, 2012). My results are challenging to the conventional wisdom that phonologization is a late-stage reanalysis of phonetic coarticulatory and perceptual effects (e.g. Ohala, 1981). Rather, it appears that phonologization occurs simultaneously with the onset of phonetic changes. I arrive at this conclusion by examining the rate of change of contextual vowel variants, and by investigating mismatches between which variants are expected to change on phonetic grounds versus phono- logical grounds. In my analysis, not only can a modular feed-forward model of phonology and phonetics account for observed patterns of phonetic change, but must be appealed to in some cases. These results revise some the facts to be explained by diachronic phonology, and I suggest the question to be pursued ought to be how phonological innovations happen when there are relatively small phonetic precursors

個人が用いる単語の意味のモデル化とその応用

学位の種別: 修士University of Tokyo(東京大学