The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The paper introduces a lightweight approach to detect code‑switching (CS) directly from speech by leveraging language‑selective hidden‑state dimensions in pretrained speech encoders. Using monolingual recordings to identify these dimensions, the method aggregates frame‑level activations to predict CS presence, achieving a mean F1 score of 0.798 on the CS‑FLEURS dataset with the Whisper‑small encoder. This demonstrates that pretrained speech models inherently encode CS information, offering a practical tool for expanding multilingual corpora.
Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding releva...
The paper surveys how code‑mixed (CM) text and speech systems transfer knowledge between CM data and the native languages involved, a process that can go in both directions. It introduces a taxonomy of transfer direction, route, and modality, and finds that most work transfers from native languages to CM, while the reverse is newer and mainly aligns the two languages. The study notes that text and speech methods overlap but rarely cite each other, and that native data often outperforms new architectures on shared speech benchmarks, highlighting open questions about data mixing and evaluation across scripts.
arXiv:2510. 05678v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages as LLMs often rely on English-centric latent representations.
arXiv:2606. 19381v1 Announce Type: cross Abstract: Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training.
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.