Probabilistic Models of Grammar Acquisition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The most convincing models of human grammar acquisition to date are supervised, in the sense that they learn from pairs of strings and meaning representations (Siskind, 1996; Villavicencio, 2002; Villavicencio, 2011; Buttery, 2004; Buttery, 2006; Kwiatkowski et al., 2012). Although the principles by which such models learn are quite general, the datasets they have been applied to have unavoidably been somewhat target-language-specific, and are also limited to discourse-external world-state-related content, contrary to the observations of (Tomasello, 2001) concerning the central role of common ground and grounding in interpersonal interaction.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Computational Models of Language Acquisition and Loss
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Number of pages1
Publication statusPublished - 2012


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