Probabilistic Models of Grammar Acquisition
Mark J. Steedman · Edinburgh Research Explorer (University of Edinburgh) · 2012
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.