arXiv cs.CL· Debajyoti Mazumder, Akash Anil·· 12 小时前AI 评分20
综述：代码混合与母语在文本和语音中的双向迁移
A Two-Way Street: Surveying the Interplay between Code-Mixing and Native Languages in Text and Speech
AI 导读
一篇系统综述梳理了代码混合（CM）与母语之间的双向迁移，覆盖文本与语音两种模态，并按迁移方向、路径和模态建立分类体系。综述发现多数迁移是从母语流向 CM 场景，反向迁移较新且主要通过语言对齐发挥作用；文本与语音研究共用方法却很少互相引用，在共享语音基准上母语数据比新架构更有帮助。
正文 · 原文
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.
| Comments: | 40 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.27631 [cs.CL] |
| (or arXiv:2610.27631v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27631 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Debajyoti Mazumder [view email]
[v1]
Tue, 20 Oct 2026 04:57:14 UTC (95 KB)
来源：arXiv cs.CL · arxiv.org