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Neural Pseudo-Relevance Feedback Models for Sparse and Dense Retrieval

Published: 07 July 2022 Publication History

Abstract

Pseudo-relevance feedback mechanisms have long served as an effective technique to improve the retrieval effectiveness in information retrieval. Recently, large pre-trained language models, such as T5 and BERT, have shown a strong capacity to capture the latent traits of texts. Given the success of these models, we seek to study the capacity of these models for query reformulation. In addition, the BERT models have demonstrated further promise for dense retrieval, where the query and documents are encoded into the contextualised embeddings and relevant documents are retrieved by conducting the semantic matching operation. Although the success of pseudo-relevance feedback for sparse retrieval is well documented, effective pseudo-relevance feedback approaches for dense retrieval paradigm are still in their infancy. Thus, we are concerned with excavating the potential of the pseudo-relevance feedback information combined with the large pre-trained models to conduct effective query reformulation operating on both sparse retrieval and dense retrieval.

References

[1]
Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In Proceedings of SIGIR. 39--48.
[2]
Craig Macdonald, Nicola Tonellotto, and Iadh Ounis. 2021. On Single and Multiple Representations in Dense Passage Retrieval. IIR 2021 Workshop (2021).
[3]
Shahrzad Naseri, Jeffrey Dalton, Andrew Yates, and James Allan. 2021. CEQE: Contextualized Embeddings for Query Expansion. Proceedings of ECIR (2021), 467--482.
[4]
Xiao Wang, Craig Macdonald, Nicola Tonellotto, and Iadh Ounis. 2021. Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval. In Proceedings of ICTIR. 297--306.
[5]
Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, and Andrew Yates. 2020. BERT-QE: Contextualized Query Expansion for Document Re-ranking. In Proceedings of EMNLP: Findings. 4718--4728.

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  • (2022)Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature SearchProceedings of the 26th Australasian Document Computing Symposium10.1145/3572960.3572980(1-10)Online publication date: 15-Dec-2022

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  1. Neural Pseudo-Relevance Feedback Models for Sparse and Dense Retrieval

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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

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    Published: 07 July 2022

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    Author Tags

    1. information retrieval
    2. pseudo-relevance feedback
    3. query expansion

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    • (2022)Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature SearchProceedings of the 26th Australasian Document Computing Symposium10.1145/3572960.3572980(1-10)Online publication date: 15-Dec-2022

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