Does language matter for spoken word classification? A multilingual generative meta-learning approach
Authors: Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der Merwe
Summary
arXiv:2605. 13084v1 Announce Type: new Abstract: Meta-learning has been shown to have better performance than supervised learning for few-shot monolingual spoken word classification.
Relevance
Read next because Does language matter for spoken word classification? A multilingual generative meta-learning approach overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, strong, under, training, does, language, model. Source: arxiv cs.CL (NLP).
Abstract
arXiv:2605.13084v1 Announce Type: new Abstract: Meta-learning has been shown to have better performance than supervised learning for few-shot monolingual spoken word classification. However, the meta-learning approach remains under-explored in multilingual spoken word classification. In this paper, we apply the Generative Meta-Continual Learning algorithm to spoken word classification. The generative nature of this algorithm makes it viable for use in application, and the meta-learning aspect promotes generalisation, which is crucial in a multilingual setting. We train monolingual models on English, German, French, and Catalan, a bilingual model on English and German, and a multilingual model on all four languages. We find that although the multilingual model performs best, the differences between model performance is unexpectedly low. We also find that the hours of unique data seen during training seems to be a stronger performance indicator than the number of languages included in the training data.