Paper: Language Acquisition and Probabilistic Models: keeping it simple

ACL ID P13-1130
Title Language Acquisition and Probabilistic Models: keeping it simple
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Hierarchical Bayesian Models (HBMs) have been used with some success to capture empirically observed pat- terns of under- and overgeneralization in child language acquisition. How- ever, as is well known, HBMs are ?ideal? learning systems, assuming ac- cess to unlimited computational re- sources that may not be available to child language learners. Conse- quently, it remains crucial to carefully assess the use of HBMs along with al- ternative, possibly simpler, candidate models. This paper presents such an evaluation for a language acquisi- tion domain where explicit HBMs have been proposed: the acquisition of En- glish dative constructions. In particu- lar, we present a detailed, empirically- grounded model-selection compari- son of HBMs vs. a simpler alternative based on clustering along...