Connectionist Models and Linguistic Theory: Investigations of Stress Systems in Language

This paper discusses a perceptron model of the learning and assignment of linguistic stress, using data from nineteen human languages. First, we point out some interesting parallels between aspects of the model and the constructs and predictions of metrical phonology, the linguistic theory of stress. Second, we develop a novel analysis of linguistic stress in terms of ease of perceptron-learnability. These two sets of results suggest that simple statistical learning techniques have the potential to complement, and provide computational validation for, abstract theoretical investigations of language. We then examine why such methodologies should be of interest for linguistic theorizing. Our analysis began at a high level by observing inherent characteristics of various stress systems, much as theoretical linguistics does. However, our explanations changed substantially when we included a detailed account of the model’s processing mechanisms. Our higher-level, theoretical account of stress was revealed as only an approximation to the lower-level computational account. Without the ability to open up the black boxes of the human processor, linguistic analyses are arguably analogous to our higher-level descriptions. This highlights the need for computational grounding of theory-building. In addition, we suggest that there are methodological problems underlying parameterbased approaches to learnability. These problems make it all the more important to seek sources of converging evidence such as is provided by computational models.