Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text
<|reference_start|>Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text: Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. Despite its widespread use online and recent research trends in this area, research in code-switching presents unique challenges, primarily stemming from the scarcity of labelled data and available resources. In this study we investigate how pre-trained Language Models handle code-switched text in three dimensions: a) the ability of PLMs to detect code-switched text, b) variations in the structural information that PLMs utilise to capture code-switched text, and c) the consistency of semantic information representation in code-switched text. To conduct a systematic and controlled evaluation of the language models in question, we create a novel dataset of well-formed naturalistic code-switched text along with parallel translations into the source languages. Our findings reveal that pre-trained language models are effective in generalising to code-switched text, shedding light on the abilities of these models to generalise representations to CS corpora. We release all our code and data including the novel corpus at https://github.com/francesita/code-mixed-probes.<|reference_end|>
@article{de leon2024code-mixed,
    title={Code-Mixed Probes Show How Pre-Trained Models Generalise On
  Code-Switched Text},
    author={Frances A. Laureano De Leon, Harish Tayyar Madabushi, Mark Lee},
    journal={arXiv preprint arXiv:2403.04872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04872},
    primaryClass={cs.CL}
}