Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training

Abstract:During language acquisition, bilingual children are regularly exposed to code-switched input and use it as a cognitive scaffold to accelerate vocabulary growth and cross-linguistic syntactic mapping. In contrast, computational bilingual models are conventionally pretrained on interleaved monolingual corpora. While introducing synthetic code-switching during pretraining has become a promising strategy to enhance cross-lingual alignment and downstream performance, the structural and developmental parameters governing the success remain poorly understood. In this work, we investigate the efficiency of training with synthetic code-switched data across two typologically distinct language pairs by controlling two key variables: the structural location of code-switches and the dynamic switching rate across training stages. Our results show that training with code-switched data improves cross-lingual alignment for typologically close languages.

Computation and Language (cs.CL)

arXiv:2610.06161 [cs.CL]

(or arXiv:2610.06161v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.06161 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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