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Current dense retrievers are not robust to out-of-domain and outlier queries, i.e.
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Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Online, 6769–6781
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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
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Distilling dense representations for ranking using tightly-coupled teachers
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Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
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Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, Online, 38–45
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Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In International Conference on Learning Representations
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RepBERT: Contextualized text embeddings for first-stage retrieval
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020 · 2020
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MS MARCO Chameleons: Challenging the MS MARCO Leaderboard with Extremely Obstinate Queries. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4426–4435
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Iain Mackie, Jeffrey Dalton, and Andrew Yates. 2021 · 2021
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Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators
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RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 5835–5847
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021 · 2021
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PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . 2173–2183
Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021a · 2021
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RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 2825–2835
Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021b · 2021
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Simple Entity-Centric Questions Challenge Dense Retrievers. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 6138–6148
Christopher Sciavolino, Zexuan Zhong, Jinhyuk Lee, and Danqi Chen. 2021 · 2021
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Are Neural Ranking Models Robust?
Chen Wu, Ruqing Zhang, Jiafeng Guo, Yixing Fan, and Xueqi Cheng. 2021 · 2021
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Optimizing Dense Retrieval Model Training with Hard Negatives. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
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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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Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval
Luyu Gao, Xueguang Ma, Jimmy J. Lin, and Jamie Callan. 2022 · 2022
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Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Shengyao Zhuang and Guido Zuccon. 2022 · 2022
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