Prange, JakobJakobPrangeDr. WONG Man Ho, IvyIvyDr. WONG Man Ho2023-11-232023-11-232023Prange, Jakob & Wong, Man Ho Ivy (2023). Reanalyzing L2 preposition learning with bayesian mixed effects and a pretrained language model. In Rogers, Anna, Boyd-Graber, Jordan & Okazaki, Naoaki (Eds.). Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers). 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023, Toronto, Canada (pp. 12722-12736). Association for Computational Linguistics.http://hdl.handle.net/20.500.11861/8704We 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.enConference Paper10.18653/v1/2023.acl-long.712