# Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model

arXiv: https://arxiv.org/abs/2302.08150 · HTML: https://mlsanity.com/paper/2302.08150/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Linguistic Knowledge and Transferability of Contextual Representations](https://mlsanity.com/paper/1903.08855/) (2019)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Event knowledge in large language models: the gap between the impossible and the unlikely](https://mlsanity.com/paper/2212.01488/) (2022)
- [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://mlsanity.com/paper/1904.11544/) (2019)
- [Comprehensive Supersense Disambiguation of English Prepositions and Possessives](https://mlsanity.com/paper/1805.04905/) (2018)
- [Natural Language Inference with Mixed Effects](https://mlsanity.com/paper/2010.10501/) (2020)
- [Can Peanuts Fall in Love with Distributional Semantics?](https://mlsanity.com/paper/2301.08731/) (2023)
- [An Item Response Theory Framework for Persuasion](https://mlsanity.com/paper/2204.11337/) (2022)
- [A Corpus of Adpositional Supersenses for Mandarin Chinese](https://mlsanity.com/paper/2003.08437/) (2020)
- [PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English](https://mlsanity.com/paper/2110.12243/) (2021)
- [Predicting the Argumenthood of English Prepositional Phrases](https://mlsanity.com/paper/1809.07889/) (2018)

## Built on by (most cited first, 1 of 1)

- [Probing the Category of Verbal Aspect in Transformer Language Models](https://mlsanity.com/paper/2406.02335/) (2024)

## Abstract (arXiv)

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.