Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866915910511493120 |
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| author | Prange, Jakob Wong, Man Ho Ivy |
| author_facet | Prange, Jakob Wong, Man Ho Ivy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_08150 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model Prange, Jakob Wong, Man Ho Ivy Computation and Language Artificial Intelligence 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. |
| title | Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2302.08150 |