Abstract
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 language | English |
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| Title of host publication | Proceedings of the Workshop on Computational Models of Language Acquisition and Loss |
| Place of Publication | Stroudsburg, PA, USA |
| Publisher | Association for Computational Linguistics |
| Pages | 19-19 |
| Number of pages | 1 |
| Publication status | Published - 2012 |