Fetching the paper…
Reading the bibliography…
In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
Earlier work this paper cites.
Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019c · 1904
Earlier work this paper cites.
Understanding the behaviors of BERT in ranking
Yifan Qiao, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. 2019 · 1904
Earlier work this paper cites.
Multi-stage document ranking with BERT
Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019b · 1910
Earlier work this paper cites.
REALM: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
Earlier work this paper cites.
Learning-to-rank with BERT in tf-ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky, and Marc Najork. 2020 · 2004
Earlier work this paper cites.
Repbert: Contextualized text embeddings for first-stage retrieval
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020 · 2006
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
Earlier work this paper cites.
Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2020a · 2007
Earlier work this paper cites.
Generation-augmented retrieval for open-domain question answering
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2020 · 2009
Earlier work this paper cites.
Answering complex open-domain questions with multi-hop dense retrieval
Wenhan Xiong, Xiang Lorraine Li, Srinivasan Iyer, Jingfei Du, Patrick S. H. Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, and Barlas Oguz. 2020b · 2009
Earlier work this paper cites.
Neural retrieval for question answering with cross-attention supervised data augmentation
Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, and Daniel Cer. 2020 · 2009
Earlier work this paper cites.
Improving efficient neural ranking models with cross-architecture knowledge distillation
Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan, and Allan Hanbury. 2020 · 2010
Earlier work this paper cites.
Neural passage retrieval with improved negative contrast
Jing Lu, Gustavo Hernández Ábrego, Ji Ma, Jianmo Ni, and Yinfei Yang. 2020 · 2010
Earlier work this paper cites.
Is retriever merely an approximator of reader?
Sohee Yang and Minjoon Seo. 2020 · 2010
Earlier work this paper cites.
Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave. 2020 · 2012
Earlier work this paper cites.
An information retrieval approach to short text conversation
Zongcheng Ji, Zhengdong Lu, and Hang Li. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Ms marco: A human-generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Cited alongside, same era.
Efficient natural language response suggestion for smart reply
Matthew L. Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil. 2017 · 2017
Cited alongside, same era.
Anserini: Enabling the use of lucene for information retrieval research
Peilin Yang, Hui Fang, and Jimmy Lin. 2017 · 2017
Cited alongside, same era.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Later among the works it cites.
Colbert: Efficient and effective passage search via contextualized late interaction over BERT
Omar Khattab and Matei Zaharia. 2020 · 2020
Later among the works it cites.
ERNIE 2.0: A continual pre-training framework for language understanding
Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2020
Later among the works it cites.
Scalable zero-shot entity linking with dense entity retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
COIL: Revisit exact lexical match in information retrieval with contextualized inverted list
Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021a · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deeper text understanding for IR with contextual neural language modeling
Zhuyun Dai and Jamie Callan. 2019 · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Learning dense representations for entity retrieval
Daniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, and Diego García-Olano. 2019 · 2019
Cited alongside, same era.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Cited alongside, same era.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Paddlepaddle: An open-source deep learning platform from industrial practice
Yanjun Ma, Dianhai Yu, Tian Wu, and Haifeng Wang. 2019 · 2019
Cited alongside, same era.
Multi-passage BERT: A globally normalized BERT model for open-domain question answering
Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019 · 2019
Cited alongside, same era.
Rethink training of BERT rerankers in multi-stage retrieval pipeline
Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021b · 2021
Closest in time.
Efficiently teaching an effective dense retriever with balanced topic aware sampling
Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin, and Allan Hanbury. 2021 · 2021
Closest in time.
UHD-BERT: bucketed ultra-high dimensional sparse representations for full ranking
Kyoungrok Jang, Junmo Kang, Giwon Hong, Sung-Hyon Myaeng, Joohee Park, Taewon Yoon, and Hee-Cheol Seo. 2021 · 2021
Closest in time.
Sparse, dense, and attentional representations for text retrieval
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2021 · 2021
Closest in time.
Reader-guided passage reranking for open-domain question answering
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2021 · 2021
Closest in time.
Rethinking search: Making experts out of dilettantes
Donald Metzler, Yi Tay, Dara Bahri, and Marc Najork. 2021 · 2021
Closest in time.
Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021 · 2021
Closest in time.
PAIR: Leveraging passage-centric similarity relation for improving dense passage retrieval
Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021 · 2021
Closest in time.
End-to-end training of neural retrievers for open-domain question answering
Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, and Bryan Catanzaro. 2021 · 2021
Closest in time.
Optimizing dense retrieval model training with hard negatives
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2021 · 2021
Closest in time.
Dense text retrieval based on pretrained language models: A survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong Wen. 2022 · 2022
Closest in time.