Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers
Author:
Affiliation:
1. University of Waterloo, Waterloo, ON, Canada
2. Toronto Metropolitan University, Toronto, ON, Canada
3. Shahrood University of Technology, Shahrood, Iran
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3583780.3615270
Reference67 articles.
1. BERT-QPP: Contextualized Pre-trained transformers for Query Performance Prediction
2. MS MARCO Chameleons: Challenging the MS MARCO Leaderboard with Extremely Obstinate Queries
3. Shallow pooling for sparse labels
4. Negar Arabzadeh , Xinyi Yan , and Charles L. A . Clarke . 2021 . Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection. CoRR abs/2109.10739 (2021). arXiv:2109.10739 https://arxiv.org/abs/2109.10739 Negar Arabzadeh, Xinyi Yan, and Charles L. A. Clarke. 2021. Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection. CoRR abs/2109.10739 (2021). arXiv:2109.10739 https://arxiv.org/abs/2109.10739
5. Neural embedding-based specificity metrics for pre-retrieval query performance prediction
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