The Credit Problem in parametric stress: A probabilistic approach
نویسندگان
چکیده
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 EDPL provides mathematically principled solution to Credit Problem (Dresher 1999). present first systematic tests of and an existing closely related model, Naïve (NPL), on full stress typology, one generated by Dresher & Kaye’s (1990) parameter framework. This framework has figured prominently in debate about necessity domain-specific mechanisms parametric stress. The essential difference between two models is incorporates mechanism directly tackles Problem, while NPL does not. find fails cope with ambiguity system both terms success data complexity, performs well metrics. Based these results, argue probabilistic inference viable domain-general approach learning, but only when involves inferential process addresses Problem. also in-depth analyses outcomes, showing how outcomes depend crucially structural ambiguities posited particular phonological theory, difficulties correspond typological gaps.
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ژورنال
عنوان ژورنال: Glossa
سال: 2021
ISSN: ['2397-1835']
DOI: https://doi.org/10.16995/glossa.5884