@inproceedings{zhang-etal-2024-modeling-bilingual,
    title = "Modeling Bilingual Sentence Processing: Evaluating {RNN} and Transformer Architectures for Cross-Language Structural Priming",
    author = "Zhang, Demi  and
      Xiao, Bushi  and
      Gao, Chao  and
      Youm, Sangpil  and
      Dorr, Bonnie J",
    editor = {S{\"a}lev{\"a}, Jonne  and
      Owodunni, Abraham},
    booktitle = "Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.mrl-1.8/",
    doi = "10.18653/v1/2024.mrl-1.8",
    pages = "127--136",
    abstract = "This study evaluates the performance of Recurrent Neural Network (RNN) and Transformer models in replicating cross-language structural priming, a key indicator of abstract grammatical representations in human language processing. Focusing on Chinese-English priming, which involves two typologically distinct languages, we examine how these models handle the robust phenomenon of structural priming, where exposure to a particular sentence structure increases the likelihood of selecting a similar structure subsequently. Our findings indicate that transformers outperform RNNs in generating primed sentence structures, with accuracy rates that exceed 25.84{\%} to 33. 33{\%}. This challenges the conventional belief that human sentence processing primarily involves recurrent and immediate processing and suggests a role for cue-based retrieval mechanisms. This work contributes to our understanding of how computational models may reflect human cognitive processes across diverse language families."
}