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Cited by 14

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval

Kong, Weize, Dudek, Jeffrey M., Li, Cheng, Zhang, Mingyang, Bendersky, Michael·2023·Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
DOI 10.1145/3539618.3592065
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Crossref · 10.1145/3539618.3592065

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Figure 1 SparseEmbed architecture overview.
Figure 1 SparseEmbed architecture overview.CC-BY · From the PDF
Figure 2 Sparse vector computation.
Figure 2 Sparse vector computation.CC-BY · From the PDF
Figure 3 Contextual embedding computation.
Figure 3 Contextual embedding computation.CC-BY · From the PDF

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Citations

Cites

Research works printed but not linked: 23

  1. Alexandr Andoni, Piotr Indyk, and Ilya Razenshteyn. Approximate nearest neighbor search in high dimensions. In Proceedings of the International Congress of Mathematicians: Rio de Janeiro 2018, pages 3287--3318. World Scientific, 2018.
  2. Yang Bai, Xiaoguang Li, Gang Wang, Chaoliang Zhang, Lifeng Shang, Jun Xu, Zhaowei Wang, Fangshan Wang, and Qun Liu. Sparterm: Learning term-based sparse representation for fast text retrieval. arXiv preprint arXiv:2010.00768, 2020. · arXiv
  3. 10.1145/1390156.1390177 · DOI · Paper page
  4. 10.1145/3397271.3401204 · DOI
  5. Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. Splade v2: Sparse lexical and expansion model for information retrieval. arXiv preprint arXiv:2109.10086, 2021. · arXiv
  6. 10.1145/3404835.3463098 · DOI
  7. 10.1145/3477495.3531857 · DOI
  8. 10.18653/v1/2022.acl-long.203 · DOI
  9. 10.18653/v1/2021.naacl-main.241 · DOI
  10. Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan, and Allan Hanbury. Improving efficient neural ranking models with cross-architecture knowledge distillation. arXiv preprint arXiv:2010.02666, 2020. · arXiv
  11. 10.1145/3404835.3462891 · DOI
  12. 10.1145/3511808.3557367 · DOI
  13. Carlos Lassance, Maroua Maachou, Joohee Park, and Stéphane Clinchant. A study on token pruning for colbert. arXiv preprint arXiv:2112.06540, 2021. · arXiv
  14. 10.18653/v1/2021.repl4nlp-1.17 · DOI
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  16. Biswajit Paria, Chih-Kuan Yeh, Ian EH Yen, Ning Xu, Pradeep Ravikumar, and Barnabás Póczos. Minimizing flops to learn efficient sparse representations. In International Conference on Learning Representations, 2019.
  17. Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2010.08191, 2020. · arXiv
  18. Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. Colbertv2: Effective and efficient retrieval via lightweight late interaction. arXiv preprint arXiv:2112.01488, 2021. · arXiv
  19. Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. BEIR: A heterogeneous benchmark for zero-shot evaluation of in- formation retrieval models. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.
  20. 10.1145/3459637.3482162 · DOI
  21. Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. Approximate nearest neighbor negative contrastive learning for dense text retrieval. arXiv preprint arXiv:2007.00808, 2020. · arXiv
  22. 10.1145/3437963.3441667 · DOI
  23. 10.1145/3269206.3271800 · DOI

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Metadata

Authors
Kong, W., Dudek, J. M., Li, C., Zhang, M., & Bendersky, M.
Affiliations
Google Research, Mountain View, CA, USA
ORCID
Venue
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
ACM
Published
2023-07-18
Pages
2399-2403
DOI
10.1145/3539618.3592065
Cited by
14
References
3
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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