Linguistic competence through the lens of grammatical variation. Part 2. Quantitative evaluation. Gerasimova, A. A., Lyutikova, E. A., & Паско, Л. И. Lomonosov Philology Journal, Moscow University Press, Moscow, 2024.
bibtex   
@article{gerasimova2024yazykovaya693873784,
    author = {Gerasimova, Anastasia A. and Lyutikova, Ekaterina A. and Паско, Л. И.},
    title = {Linguistic competence through the lens of grammatical variation. Part 2. Quantitative evaluation},
    journal = {Lomonosov Philology Journal},
    year = {2024},
    number = {5},
    issn = {0130-0075},
    pages = {111--128},
    publisher = {Moscow University Press},
    address = {Moscow},
    annote = {This paper discusses the distribution of acceptability ratings in native speakers’ judgments about grammatical phenomena displaying variability with particular focus on the consistency of the data obtained in the context of a syntactic experiment. We conduct a quantitative analysis of experimental data on four phenomena that vary with respect to the expected distribution of variants and the type of grammatical interaction. The quantitative assessment includes two parameters: the agreement of respondents within the linguistic community and the individual consistency within an experimental trial.
The analysis shows that the availability of one or more variants of a certain construction in the grammar does not correlate in any way with the consistency patterns. At the same time, the actual distribution of grammatical profiles of native speakers turns out to be much more complicated than it follows from the generalizations made for the entire population of participants in the experiment. We conclude that the proposed consistency parameters play an equally important role in the distribution of judgments that grammatical predictors do, and therefore are necessary for modeling linguistic competence both in linguistic theories and in neural network architectures. Finally, the identified characteristics of acceptability in the context of variation provide a baseline for the analysis of the linguistic competence of neural networks and its comparison with the linguistic competence of a human.},
    language = {russian},
    keywords = {in Russian}
}

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