Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models.

@inproceedings{cohen-2021-relevance, author = {Daniel Cohen and Bhaskar Mitra and Oleg Lesota and Navid Rekabsaz and Carsten Eickhoff}, editor = {Fernando Diaz and Chirag Shah and Torsten Suel and Pablo Castells and Rosie Jones and Tetsuya Sakai}, title = {{Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models}}, booktitle = {{SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021}}, pages = {654--664}, publisher = {ACM}, year = {2021}, url = {https://doi.org/10.1145/3404835.3462951}, doi = {https://doi.org/10.1145/3404835.3462951} }