The pre-trained multi-lingual XLSR model generalizes well for language identification after fine-tuning on unseen languages. However, the performance significantly degrades when the languages are not very distinct from each other, for example, in the case of dialects. Low resource dialect classification remains a challenging problem to solve. We present a new data augmentation method that leverages model training dynamics of individual data points to improve sampling for latent mixup. The method works well in low-resource settings where generalization is paramount. Our datamaps-based mixup technique, which we call Map-Mix improves weighted F1 scores by 2% compared to the random mixup baseline and results in a significantly well-calibrated model. The code for our method is open sourced on https://github.com/skit-ai/Map-Mix.

翻译：预训练的多语言XLSR模型在针对未见语言进行微调后，能够很好地泛化用于语言身份识别。然而，当语言之间差异不大时（例如方言情况），其性能会显著下降。低资源方言分类仍然是一个亟待解决的难题。我们提出了一种新的数据增强方法，该方法利用单个数据点的模型训练动态信息来改进潜在混合采样。该方法在泛化至关重要的低资源场景中表现良好。我们基于数据图的混合技术（称为Map-Mix）相比随机混合基线，将加权F1分数提高了2%，并得到显著校准的模型。该方法的代码已在https://github.com/skit-ai/Map-Mix开源。