no code implementations • 13 Apr 2024 • Yifan Qiao, Shanxiu He, Yingrui Yang, Parker Carlson, Tao Yang
This paper revisits cluster-based retrieval that partitions the inverted index into multiple groups and skips the index partially at cluster and document levels during online inference using a learned sparse representation.
1 code implementation • 20 Jun 2023 • Yifan Qiao, Yingrui Yang, Shanxiu He, Tao Yang
Learned sparse document representations using a transformer-based neural model has been found to be attractive in both relevance effectiveness and time efficiency.
1 code implementation • 2 May 2023 • Yifan Qiao, Yingrui Yang, Haixin Lin, Tao Yang
Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpact.
1 code implementation • 23 Apr 2022 • Yifan Qiao, Yingrui Yang, Haixin Lin, Tianbo Xiong, Xiyue Wang, Tao Yang
This paper proposes a dual skipping guidance scheme with hybrid scoring to accelerate document retrieval that uses learned sparse representations while still delivering a good relevance.
no code implementations • ACL 2022 • Yingrui Yang, Yifan Qiao, Tao Yang
Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens.
no code implementations • 11 Mar 2021 • Yingrui Yang, Yifan Qiao, Jinjin Shao, Mayuresh Anand, Xifeng Yan, Tao Yang
By applying token encoding on top of a dual-encoder architecture, BECR separates the attentions between a query and a document while capturing the contextual semantics of a query.
no code implementations • 19 Mar 2019 • Yingrui Yang, Molin Wang
However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem.
no code implementations • 27 Feb 2019 • Peng Jiang, Yingrui Yang, Gann Bierner, Fengjie Alex Li, Ruhan Wang, Azadeh Moghtaderi
Genealogy research is the study of family history using available resources such as historical records.