  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.
