@inproceedings{prange-wong-2023-reanalyzing,
    title = "Reanalyzing {L}2 Preposition Learning with {B}ayesian Mixed Effects and a Pretrained Language Model",
    author = "Prange, Jakob  and
      Wong, Man Ho Ivy",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.712",
    doi = "10.18653/v1/2023.acl-long.712",
    pages = "12722--12736",
    abstract = "We use both Bayesian and neural models to dissect a data set of Chinese learners{'} pre- and post-interventional responses to two tests measuring their understanding of English prepositions. The results mostly replicate previous findings from frequentist analyses and newly reveal crucial interactions between student ability, task type, and stimulus sentence. Given the sparsity of the data as well as high diversity among learners, the Bayesian method proves most useful; but we also see potential in using language model probabilities as predictors of grammaticality and learnability.",
}

We use both Bayesian and neural models to dissect a data set of Chinese learners{'} pre- and post-interventional responses to two tests measuring their understanding of English prepositions. The results mostly replicate previous findings from frequentist analyses and newly reveal crucial interactions between student ability, task type, and stimulus sentence. Given the sparsity of the data as well as high diversity among learners, the Bayesian method proves most useful; but we also see potential in using language model probabilities as predictors of grammaticality and learnability.
Prange, Jakob Wong, Man Ho Ivy
Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model