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Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be datahungry and require large amounts of data even for fine tuning. This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem. We perform data augmentation by creating training data using parts ofdoi:10.1145/3539813.3545139 fatcat:neecudlkwvgfbmzpnh2zddxe7u