2022/04/25 by Xiaoyang Chen, Ben He, Chen, Xiaoyang +3
Computer Science · Decision Sciences · #Data Management and Algorithms #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Topic Modeling #cs.IR
paper · pdf · doi:10.48550/arxiv.2204.11489
Accepted at Proceedings of the 44th European Conference on Information Retrieval, ECIR 2022
arxiv created 2022/04/25 · openalex publication_date 2022/04/25 · arxiv updated 2022/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While large-scale pre-trained language models like BERT have advanced the state-of-the-art in IR, its application in query performance prediction (QPP) is so far based on pointwise modeling of individual queries. Meanwhile, recent studies suggest that the cross-attention modeling of a group of documents can effectively boost performances for both learning-to-rank algorithms and BERT-based re-ranking. To this end, a BERT-based groupwise QPP model is proposed, in which the ranking contexts of a list of queries are jointly modeled to predict the relative performance of individual queries. Extensive experiments on three standard TREC collections showcase effectiveness of our approach. Our code is available at https://github.com/VerdureChen/Group-QPP.