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Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field.
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Bias-variance decomposition of ir evaluation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval . 1021–1024
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Explaining and harnessing adversarial examples
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Parameters learned in the comparison of retrieval models using term dependencies
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Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
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Bias–variance analysis in estimating true query model for information retrieval
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A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . 55–64
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Deeper Text Understanding for IR with Contextual Neural Language Modeling. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, 985–988
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MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching. In Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19) . ACM, New York, NY, USA, 1297–1300
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Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. 2019 · 2019
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Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In Proceedings of the 10th ACM workshop on artificial intelligence and security . 15–26
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh. 2017 · 2017
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A large-scale query spelling correction corpus. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1261–1264
Matthias Hagen, Martin Potthast, Marcel Gohsen, Anja Rathgeber, and Benno Stein. 2017 · 2017
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Learning to match using local and distributed representations of text for web search. In Proceedings of the 26th International Conference on World Wide Web . 1291–1299
Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017 · 2017
Cited alongside, same era.
Deeprank: A new deep architecture for relevance ranking in information retrieval. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 257–266
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
Information retrieval meets game theory: The ranking competition between documents’ authors. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . 465–474
Nimrod Raifer, Fiana Raiber, Moshe Tennenholtz, and Oren Kurland. 2017 · 2017
Cited alongside, same era.
Towards crafting text adversarial samples
Suranjana Samanta and Sameep Mehta. 2017 · 2017
Cited alongside, same era.
End-to-end neural ad-hoc ranking with kernel pooling. In Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval . 55–64
Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017 · 2017
Cited alongside, same era.
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Multi-head multi-layer attention to deep language representations for grammatical error detection
Masahiro Kaneko and Mamoru Komachi. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT . 4171–4186
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
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The neural hype and comparisons against weak baselines. In ACM SIGIR Forum , Vol. 52. ACM New York, NY, USA, 40–51
Jimmy Lin. 2019 · 2019
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Rodrigo Nogueira and Kyunghyun Cho. 2019 · 2019
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Combating Adversarial Misspellings with Robust Word Recognition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 5582–5591
Danish Pruthi, Bhuwan Dhingra, and Zachary C Lipton. 2019 · 2019
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Data augmentation for bert fine-tuning in open-domain question answering
Wei Yang, Yuqing Xie, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 2019
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Cross-domain modeling of sentence-level evidence for document retrieval. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3481–3487
Zeynep Akkalyoncu Yilmaz, Wei Yang, Haotian Zhang, and Jimmy Lin. 2019 · 2019
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Probabilistic Jacobian-based Saliency Maps Attacks
Théo Combey, António Loison, Maxime Faucher, and Hatem Hajri. 2020 · 2020
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Pretrained Transformers Improve Out-of-Distribution Robustness. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 2744–2751
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song. 2020 · 2020
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Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking
Sebastian Hofstätter, Markus Zlabinger, and Allan Hanbury. 2020 · 2020
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 8018–8025
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
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Robust Encodings: A Framework for Combating Adversarial Typos. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 2752–2765
Erik Jones, Robin Jia, Aditi Raghunathan, and Percy Liang. 2020 · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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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 . 39–48
Omar Khattab and Matei Zaharia. 2020 · 2020
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Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?. In International Conference on Learning Representations
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2020 · 2020
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Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Muhammad Shafique, Mahum Naseer, Theocharis Theocharides, Christos Kyrkou, Onur Mutlu, Lois Orosa, and Jungwook Choi. 2020 · 2020
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Capreolus: A toolkit for end-to-end neural ad hoc retrieval. In Proceedings of the 13th International Conference on Web Search and Data Mining . 861–864
Andrew Yates, Siddhant Arora, Xinyu Zhang, Wei Yang, Kevin Martin Jose, and Jimmy Lin. 2020 · 2020
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Machine learning testing: Survey, landscapes and horizons
Jie M Zhang, Mark Harman, Lei Ma, and Yang Liu. 2020 · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 8340–8349
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2021
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Wilds: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning . PMLR, 5637–5664
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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B-PROP: Bootstrapped Pre-Training with Representative Words Prediction for Ad-Hoc Retrieval
Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Yingyan Li, and Xueqi Cheng. 2021 · 2021
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Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators
Gustavo Penha, Arthur Câmara, and Claudia Hauff. 2021 · 2021
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Bias-Variance Decomposition for Ranking. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 472–480
Pannaga Shivaswamy and Ashok Chandrashekar. 2021 · 2021
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Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing
Shailesh Tripathi, David Muhr, Manuel Brunner, Herbert Jodlbauer, Matthias Dehmer, and Frank Emmert-Streib. 2021 · 2021
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Grey-box Adversarial Attack And Defence For Sentiment Classification. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 4078–4087
Ying Xu, Xu Zhong, Antonio Jimeno Yepes, and Jey Han Lau. 2021 · 2021
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Practical Relative Order Attack in Deep Ranking. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 16413–16422
Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Yinghui Xu, Nanning Zheng, and Gang Hua. 2021 · 2021
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Dealing with Typos for BERT-based Passage Retrieval and Ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 2836–2842
Shengyao Zhuang and Guido Zuccon. 2021 · 2021
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
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Adversarial Examples for Evaluating Reading Comprehension Systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . 2021–2031
Robin Jia and Percy Liang. 2017 · 2031
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Speaker-aware bert for multi-turn response selection in retrieval-based chatbots. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2041–2044
Jia-Chen Gu, Tianda Li, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei, and Xiaodan Zhu. 2020 · 2044
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