Fetching the paper…
Reading the bibliography…
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score.
The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 1904
Earlier work this paper cites.
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 1908
Earlier work this paper cites.
A vector space model for automatic indexing
Gerard Salton, Anita Wong, and Chung-Shu Yang. 1975 · 1975
Earlier work this paper cites.
A language modeling approach to information retrieval. In Proc. of the 21st annual ACM SIGIR conference on Research and development in information retrieval . ACM, 275–281
Jay M Ponte and W Bruce Croft. 1998 · 1998
Earlier work this paper cites.
Statistical language models for information retrieval
ChengXiang Zhai. 2008 · 2008
Earlier work this paper cites.
Understanding Neural Abstractive Summarization Models via Uncertainty
Jiacheng Xu, Shrey Desai, and Greg Durrett. 2020 · 2010
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data. In Proc. of the ACM Conference on Information and Knowledge Management
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013 · 2013
Earlier work this paper cites.
A deep architecture for matching short texts. In Proc. of the Conference on Advances in Neural Information Processing Systems
Zhengdong Lu and Hang Li. 2013 · 2013
Earlier work this paper cites.
Convolutional neural network architectures for matching natural language sentences. In Proc. of the Conference on Advances in Neural Information Processing Systems
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proc. of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Learning semantic representations using convolutional neural networks for web search. In Proc. of the Conference on World Wide Web
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks. In Proc. of the Conference on Advances in Neural Information Processing Systems
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Oriol Vinyals and Quoc Le. 2015 · 2015
Earlier work this paper cites.
Normalized word embedding and orthogonal transform for bilingual word translation. In Proc. of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015 · 2015
Earlier work this paper cites.
Learning to reweight terms with distributed representations. In Proc. of the ACM SIGIR Conference on Research and Development in Information Retrieval
Guoqing Zheng and Jamie Callan. 2015 · 2015
Earlier work this paper cites.
Query Expansion with Locally-Trained Word Embeddings. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Vol. 1. 367–377
Fernando Diaz, Bhaskar Mitra, and Nick Craswell. 2016 · 2016
Earlier work this paper cites.
A deep relevance matching model for ad-hoc retrieval. In Proc. of the ACM on Conference on Information and Knowledge Management
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016 · 2016
Earlier work this paper cites.
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning . 280–290
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
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.
Text Matching as Image Recognition.. In Proc. of the AAAI Conference on Artificial Intelligence
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2016 · 2016
Cited alongside, same era.
Generalizing translation models in the probabilistic relevance framework. In Proc. of the ACM on Conference on Information and Knowledge Management
Navid Rekabsaz, Mihai Lupu, Allan Hanbury, and Guido Zuccon. 2016 · 2016
Cited alongside, same era.
PACRR: A Position-Aware Neural IR Model for Relevance Matching. In Proc. of the Conference on Empirical Methods in Natural Language Processing
Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2017 · 2017
Cited alongside, same era.
Learning to match using local and distributed representations of text for web search. In Proc. of the Conference on World Wide Web
An Assumption-Free Approach to the Dynamic Truncation of Ranked Lists. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) . ACM, 79–82
Yen-Chieh Lien, Daniel Cohen, and W. Bruce Croft. 2019 · 2019
Later among the works it cites.
Text Summarization with Pretrained Encoders. 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) . 3721–3731
Yang Liu and Mirella Lapata. 2019 · 2019
Later among the works it cites.
Contextualized Word Representations for Document Re-Ranking. In Proc. of the ACM SIGIR conference on Research and Development in Information Retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. 2019 · 2019
Later among the works it cites.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017 · 2017
Cited alongside, same era.
Get To The Point: Summarization with Pointer-Generator Networks. In Proc. of the Annual Meeting of the Association for Computational Linguistics
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Proc. of the Conference on Advances in Neural Information Processing Systems
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
End-to-end neural ad-hoc ranking with kernel pooling. In Proc. of the ACM SIGIR Conference on Research and Development in Information Retrieval
Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017 · 2017
Cited alongside, same era.
Multi-Task Learning For Document Ranking And Query Suggestion. In Sixth International Conference on Learning Representations
Wasi Uddin Ahmad and Kai-Wei Chang. 2018 · 2018
Cited alongside, same era.
Convolutional neural networks for soft-matching n-grams in ad-hoc search. In Proc. of the ACM Conference on Web Search and Data Mining
Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu. 2018 · 2018
Cited alongside, same era.
Modeling diverse relevance patterns in ad-hoc retrieval. In The 41st ACM SIGIR Conference on Research and Development in Information Retrieval
Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Chengxiang Zhai, and Xueqi Cheng. 2018 · 2018
Cited alongside, same era.
Co-PACRR: A context-aware neural IR model for ad-hoc retrieval. In Proc. of the ACM Conference on Web Search and Data Mining
Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2018 · 2018
Cited alongside, same era.
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019 · 2019
Later among the works it cites.
A hybrid retrieval-generation neural conversation model. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 1341–1350
Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W Bruce Croft, Xiaodong Liu, Yelong Shen, and Jingjing Liu. 2019 · 2019
Later among the works it cites.
Brown University at TREC Deep Learning 2019. In Proceedings of the 28th Text Retrieval Conference (TREC) . NIST
George Zerveas, Ruochen Zhang, Leila Kim, and Carsten Eickhoff. 2019 · 2019
Later among the works it cites.
Choppy: Cut Transformer for Ranked List Truncation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20) . 1513–1516
Dara Bahri, Yi Tay, Che Zheng, Donald Metzler, and Andrew Tomkins. 2020 · 2020
Later among the works it cites.
Overview of the trec 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees. 2020 · 2020
Later among the works it cites.
Beyond [CLS] through Ranking by Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 1722–1727
Cicero dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, and Bing Xiang. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Efficient document re-ranking for transformers by precomputing term representations. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 49–58
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, and Ophir Frieder. 2020 · 2020
Later among the works it cites.
Document Ranking with a Pretrained Sequence-to-Sequence Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings . 708–718
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020
Later among the works it cites.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Later among the works it cites.
Generating clarifying questions for information retrieval. In Proceedings of The Web Conference 2020 . 418–428
Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020 · 2020
Later among the works it cites.
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, and Carsten Eickhoff. 2021 · 2021
Closest in time.
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In Proceedings of the International Conference on Learning Representations
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwikj. 2021 · 2021
Closest in time.
Deep Query Likelihood Model for Information Retrieval. In Proceedings of the 43rd European Conference on IR Research, ECIR 2021, Virtual Event . Springer, 463–470
Shengyao Zhuang, Hang Li, and Guido Zuccon. 2021 · 2021
Closest in time.