Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text
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arXiv
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| Format: | Preprint |
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2024
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| author | De Leon, Frances A. Laureano Madabushi, Harish Tayyar Lee, Mark |
| author_facet | De Leon, Frances A. Laureano Madabushi, Harish Tayyar Lee, Mark |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text De Leon, Frances A. Laureano Madabushi, Harish Tayyar Lee, Mark Computation and Language 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. |
| title | Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.04872 |