Comparing Theories of Speaker Choice Using a Model of Classifier Production in Mandarin Chinese
Name
N18-1181.pdf
Size
276.97 KB
Format
Adobe PDF
Checksum (MD5)
5bfbfbc8b9b0a3676ca842c30c77a057
Author(s)
Zhan, Meilin
Levy, Roger P
Date Issued
2018
Journal
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Publisher
Association for Computational Linguistics
Citation
Zhan, Meilin and Levy, Roger. "Comparing Theories of Speaker Choice Using a Model of Classifier Production in Mandarin Chinese." Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), June 2018, New Orleans, Louisiana, Association for Computational Linguistics, 2018 © 2018 The Association for Computational Linguistics
Version
Final published version
Abstract
Speakers often have more than one way to express the same meaning. What general principles govern speaker choice in the face of optionality when near semantically invariant alternation exists? Studies have shown that optional reduction in language is sensitive to contextual predictability, such that more predictable a linguistic unit is, the more likely it is to get reduced. Yet it is unclear whether these cases of speaker choice are driven by audience design versus toward facilitating production. Here we argue that for a different optionality phenomenon, namely classifier choice in Mandarin Chinese, Uniform Information Density and at least one plausible variant of availability-based production make opposite predictions regarding the relationship between the predictability of the upcoming material and speaker choices. In a corpus analysis of Mandarin Chinese, we show that the distribution of speaker choices supports the availability-based production account and not the Uniform Information Density.
MIT Department
Massachusetts Institute of Technology. Department of Linguistics and Philosophy
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.18653/v1/n18-1181
