Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text

• URL: http://arxiv.org/abs/2403.04872v2
• Date: Tue, 7 May 2024 16:38:25 GMT
• Title: Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text
• Authors: Frances A. Laureano De Leon, Harish Tayyar Madabushi, Mark Lee,
• Abstract summary: We investigate how pre-trained Language Models handle code-switched text in three dimensions. Our findings reveal that pre-trained language models are effective in generalising to code-switched text.
• Score: 1.9185059111021852
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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