Abstract
In this paper, we introduce a novel domain-general, statistical learning model for P&P grammars: the Expectation Driven Parameter Learner (EDPL). We show that the EDPL provides a mathematically principled solution to the Credit Problem (Dresher 1999). We present the first systematic tests of the EDPL and an existing and closely related model, the Naïve Parameter Learner (NPL), on a full stress typology, the one generated by Dresher & Kaye’s (1990) stress parameter framework. This framework has figured prominently in the debate about the necessity of domain-specific mechanisms for learning of parametric stress. The essential difference between the two learning models is that the EDPL incorporates a mechanism that directly tackles the Credit Problem, while the NPL does not. We find that the NPL fails to cope with the ambiguity of this stress system both in terms of learning success and data complexity, while the EDPL performs well on both metrics. Based on these results, we argue that probabilistic inference provides a viable domain-general approach to parametric stress learning, but only when learning involves an inferential process that directly addresses the Credit Problem. We also present in-depth analyses of the learning outcomes, showing how learning outcomes depend crucially on the structural ambiguities posited by a particular phonological theory, and how these learning difficulties correspond to typological gaps.
Highlights
Understanding how learners overcome the pervasive ambiguity inherent to the language acquisition process is a foundational question of linguistics, and cognitive science more generally
We present systematic tests evaluating the performance of both the Expectation Driven Parameter Learner (EDPL) and a simpler learning model, the Naïve Parameter Learner (NPL; Yang 2002), on the complete typology of 302 stress systems defined by Dresher & Kaye’s 11 parameters, the first systematic tests of either model on a complete stress typology
We present the first systematic tests of both the NPL and EDPL on a full stress typology, namely, the one predicted by Dresher & Kaye’s (D&K; 1990) framework
Summary
Understanding how learners overcome the pervasive ambiguity inherent to the language acquisition process is a foundational question of linguistics, and cognitive science more generally. This approach has primarily been explored in the context of P&P syntax (Fodor 1998; Sakas & Fodor 2001; Pearl 2007; Pearl & Lidz 2009; see Sakas 2016 for an overview), one strand of this approach – relying on domain-specific mechanisms to reduce ambiguity – has been explored in P&P phonology (Dresher & Kaye 1990; Pearl 2007; 2011) Another approach to the Credit Problem exploits the structure of the grammatical framework, as in the classic Optimality Theoretic (OT; Prince & Smolensky 1993/2004) solution to learning rankings using Error-Driven Constraint Demotion (Tesar 1995; see Tesar 2013 for an approach relying on the structure of Output-Driven Maps). Clark’s (1989; 1992) work on genetic algorithms may be classified under this general approach
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