We propose a trasductive approach for few shot cross-lingual classification.
We propose AugVic, a data augmentation framework for sequence to sequence model (i.e. NMT) using Language Model.
We propose UXLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios.
We propose a novel semi-supervised method to learn cross-lingual word embeddings for BLI.
We propose a superior model and training method for zero resource transfer of Cross-lingual Named Entity Recognition.