Efficient Neural Document Ranking with Compact Representations

---

Project Overview

This project studies efficiency optimization in neural ranking for document search.

---

People

• Parker Carlson
• Shanxiu He
• Yifan Qiao
• Wentai Xie
• Yingrui Yang
• Tao Yang

---

Publications

• Y. Qiao, P. Carlson, S. He, Y. Yang, T. Yang, Threshold-driven Pruning with Segmented Maximum Term Weights for Approximate Cluster-based Sparse Retrieval. EMNLP 2024 (2024 Conference on Empirical Methods in Natural Language Processing). Outsanding paper award. An earlier version appeared in axXiv 2404.08896 April 2024

• Y. Yang, S. He, T. Yang, On Adaptive Knowledge Distillation with Generalized KL-Divergence Loss for Ranking Model Refinement. ICTIR 2024 (10th ACM SIGIR/14th International Conference on the Theory of Information Retrieval)

• Y. Yang, Y. Qiao, S. He and T. Yang, Weighted KL-Divergence for Document Ranking Model Refinement. SIGIR 2024 (47th International ACM SIGIR Conference on Research and Development in Information Retrieval)

• Y. Yang, P. Carlson, S. He, Y. Qiao and T. Yang, Cluster-based Partial Dense Retrieval Fused with Sparse Text Retrieval. SIGIR 2024 (47th International ACM SIGIR Conference on Research and Development in Information Retrieval)

• Y. Qiao, S. Ji, C. Wang, J. Shao, T. Yang. Privacy-aware Document Retrieval with Two-level Inverted Indexing Information Retrieval Journal. No. 12, Nov 2023. PDF

• Y. Qiao, Y. Yang, S. He and T. Yang, Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval. Proc. of ACM conference on Research and Development in Information Retrieval, SIGIR'2023 .

• Y. Qiao, Y. Yang, H. Lin, T. Yang, Optimizing Guided Traversal for Fast Learned Sparse Retrieval, Proc. of the ACM Web Conference 2023 (WWW ’23), May 1–5, 2023, Austin, TX, USA . PDF. Slides

• Y. Yang, S. He, Y. Qiao, W. Xie, and T. Yang, Balanced Knowledge Distillation with Contrastive Learning for Document Re-ranking. Proc. of 9th ACM SIGIR/13th Inter. Conference on the Theory of Information Retrieval (ICTIR 2023).

---

Acknowledgment: This project is supported in part by NSF 2225942 (2022-2025). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.