Stability of Syntactic Dialect Classification over Space and Time

Jonathan Dunn, Sidney Wong


Abstract
This paper analyses the degree to which dialect classifiers based on syntactic representations remain stable over space and time. While previous work has shown that the combination of grammar induction and geospatial text classification produces robust dialect models, we do not know what influence both changing grammars and changing populations have on dialect models. This paper constructs a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities. Syntactic representations are formulated within the usage-based Construction Grammar paradigm (CxG). The decay rate of classification performance for each dialect over time allows us to identify regions undergoing syntactic change. And the distribution of classification accuracy within dialect regions allows us to identify the degree to which the grammar of a dialect is internally heterogeneous. The main contribution of this paper is to show that a rigorous evaluation of dialect classification models can be used to find both variation over space and change over time.
Anthology ID:
2022.coling-1.3
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
26–36
Language:
URL:
https://aclanthology.org/2022.coling-1.3
DOI:
Bibkey:
Cite (ACL):
Jonathan Dunn and Sidney Wong. 2022. Stability of Syntactic Dialect Classification over Space and Time. In Proceedings of the 29th International Conference on Computational Linguistics, pages 26–36, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Stability of Syntactic Dialect Classification over Space and Time (Dunn & Wong, COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.3.pdf