[14] Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, and Bing Xiang. Beyond [CLS] through Ranking by Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 1722-1727, 2020.
[15] D.A. Edwards. On the Kantorovich-Rubinstein theorem. Expositiones Mathematicae, 29(4): 387-398, 2011.
[16] Yanai Elazar and Yoav Goldberg. Adversarial Removal of Demographic Attributes from Text Data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 11-21, 2018.
[17] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky. Domain-Adversarial Training of Neural Networks. Journal of Machine Learning Research, 17(59):1-35, 2016.
[18] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative Adversarial Nets. In Advances in Neural Information Processing Systems, 2014.
[19] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola. A Kernel Two-Sample Test. Journal of Machine Learning Research, 13(25):723-773, 2012.
[20] John Guiver and Edward Snelson. Bayesian inference for Plackett-Luce ranking models. In Proceedings of the 26th International Conference on Machine Learning, pages 377-384, 2009.
[21] Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, and Xueqi Cheng. A Deep Look into Neural Ranking Models for Information Retrieval. Information Processing & Management, 57(6), 2020.
[22] Shuguang Han, Xuanhui Wang, Mike Bendersky, and Marc Najork. Learning-to-Rank with BERT in TF-Ranking, 2020. arXiv:2004.08476 [cs.IR].
[23] Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, and Donald Metzler. ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3747–3758, 2022.
[24] Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky, and Marc Najork. On Optimizing Top-K Metrics for Neural Ranking Models. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2303-2307, 2022.
[25] Rolf Jagerman, Xuanhui Wang, Honglei Zhuang, Zhen Qin, Michael Bendersky, and Marc Najork. Rax: Composable Learning-to-Rank Using JAX. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3051-3060, 2022.
[26] Kalervo Järvelin and Jaana Kekäläinen. Cumulated Gain-Based Evaluation of IR Techniques. ACM Transactions on Information Systems, 20(4):422-446, 2002.
[27] Thorsten Joachims. Training Linear SVMs in Linear Time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 217-226, 2006.
[28] Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 6769–6781, 2020.
[29] Omar Khattab and Matei Zaharia. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 39-48, 2020.