Data Augmentation for Sample Efficient and Robust Document Ranking.

The article focuses on enhancing contextual ranking models by proposing data augmentation methods to improve ranking performance effectively and robustly. It mentions by utilizing supervised and unsupervised augmentation schemes, along with contrastive losses adapted for ranking tasks, the study demonstrates significant performance improvements, particularly in sample efficiency and robustness across in-domain and out-of-domain benchmarks.