|Title:||Gradient Grammaticality as an Effect of Selective Constraint Re-ranking|
|Comment:||In M. Catherine Gruber, Derrick Higgins, Kenneth S. Olson, and Tamra Wysocki, eds., Papers from the 34th Meeting of the Chicago Linguistic Society. Vol. 2: The Panels, 95-109. Chicago, 1998.|
|Abstract:||The validity of a grammatical framework can be verified in at least the three ways: (a) by showing its applicability to wide range of linguistic phenomena, (b) by demonstrating the soundness of its formal foundations, and (c) by verifying its compatibility with experimental evidence. As for Optimality Theory (OT; Prince and Smolensky 1993), option (a) has been pursued extensively in the recent phonological literature (and to a lesser extend in the syntactic literature). Also option (b) has been the the topic of some research (e.g., Ellison 1994, Karttunen 1998). However, no attempts have been made so far to test the concepts and mechanisms assumed in OT against experimental evidence.
The present paper attempts to fill this gap by testing OT against evidence from what is probably the most natural empirical domain for a linguistic framework: grammaticality judgments. More specifically, we focus on the phenomenon of gradience in linguistic data. We argue that gradient data can serve as a tool for evaluating the status of suboptimal candidates in OT, an approach that allows to scrutinize OT's concepts of constraint ranking and constraint interaction. The experimental data we present show that constraint violations are cumulative, and that two types of constraints have to be distinguished: hard and soft ones. These results lend limited support to the notion of constraint ranking assumed in OT, and seem compatible with OT's concept of strict domination of constraints.
The second part of this paper deals with the theoretical issues arising from an attempt to model gradient linguistic data in OT. We show that a naive model that equates relative grammaticality with relative optimality is not tenable, and propose an alternative approach based on the concept of selective constraint re-ranking. This approach, which is grounded in OT learnability theory, predicts the cumulativity of constraint violations, and allows to model the distinction between hard and soft constraints, thus accounting for the experimental findings.