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The survey covers how models for code-mixed text and speech borrow from, and feed back into, models for the languages being mixed. It usefully maps transfer direction, route, and modality, and reports that native-language data often helps more than new architectures on a shared speech benchmark. Readers should note that it is a literature synthesis with one benchmark comparison, so its claims about where progress lies are directional, and the open questions it raises (data mixing ratios, evaluation across scripts) remain unresolved.
A Two-Way Street: Surveying the Interplay between Code-Mixing and Native Languages in Text and Speech
arXiv:2610.27631v1 Announce Type: new Abstract: Systems for code-mixed (CM) text and speech rarely start from scratch. CM data is scarce, so most work borrows from the languages being mixed, which we call native languages. This transfer can also run in reverse, when CM data is used to improve models for the native languages. Existing surveys focus on processing CM itself, and none brings the two directions together. This paper fills that gap with a systematic survey of transfer in both directions, across text and speech. We organise the surveyed work in a taxonomy of transfer direction, route and modality. Through this taxonomy, we find that most transfer runs from native languages into the CM setting. The reverse direction is younger, and in it code-switching mainly helps by aligning the two languages. Text and speech work share methods but rarely cite each other. On a shared speech benchmark, native data helps more than new architectures. These findings point to open challenges, such as how much native and CM data to mix in training, and how to evaluate CM text written in different scripts.
Abstract:Systems for code-mixed (CM) text and speech rarely start from scratch. CM data is scarce, so most work borrows from the languages being mixed, which we call native languages. This transfer can also run in reverse, when CM data is used to improve models for the native languages. Existing surveys focus on processing CM itself, and none brings the two directions together. This paper fills that gap with a systematic survey of transfer in both directions, across text and speech. We organise the surveyed work in a taxonomy of transfer direction, route and modality. Through this taxonomy, we find that most transfer runs from native languages into the CM setting. The reverse direction is younger, and in it code-switching mainly helps by aligning the two languages. Text and speech work share methods but rarely cite each other. On a shared speech benchmark, native data helps more than new architectures. These findings point to open challenges, such as how much native and CM data to mix in training, and how to evaluate CM text written in different scripts.
Computation and Language (cs.CL)
arXiv:2610.27631 [cs.CL]
(or arXiv:2610.27631v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.27631 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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