@inproceedings{zhang-etal-2026-efficient-framework,
title = "An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision",
author = "Zhang, Zishuai and
Yu, Sihao and
Xiewenyi and
Nie, Ying and
Wang, Junfeng and
Zheng, Zhiming and
Yin, Dawei and
Zhang, Hainan",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.67/",
pages = "966--978",
ISBN = "979-8-89176-394-4",
abstract = "The whole-page reranking integrates retrieval results from multiple modalities and is critical for user experience of search engines, yet it requires costly large-scale expert annotations due to the complexity of assessing cross-modal relevances. In this paper, we propose SMAR, a novel whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross modal annotations and distilling intra-modality preferences to align relevance scales across modalities. Specifically, we use pre-trained single-modal rankers to construct candidate pages for limited cross-modal annotation at the page level. The whole-page reranker is then trained on these samples, enforcing consistency with single-modal preferences to preserve intra-modal ranking quality. Experiments on the Qilin and CrossRank datasets demonstrate that SMAR reduces annotation costs by 70-90{\%} while outperforming the fully-annotated reranking baselines. Further offline and online A/B tests confirm significant gains in both ranking metrics and user experience, validating the effectiveness and practical value of our approach in real-world search scenarios."
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<abstract>The whole-page reranking integrates retrieval results from multiple modalities and is critical for user experience of search engines, yet it requires costly large-scale expert annotations due to the complexity of assessing cross-modal relevances. In this paper, we propose SMAR, a novel whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross modal annotations and distilling intra-modality preferences to align relevance scales across modalities. Specifically, we use pre-trained single-modal rankers to construct candidate pages for limited cross-modal annotation at the page level. The whole-page reranker is then trained on these samples, enforcing consistency with single-modal preferences to preserve intra-modal ranking quality. Experiments on the Qilin and CrossRank datasets demonstrate that SMAR reduces annotation costs by 70-90% while outperforming the fully-annotated reranking baselines. Further offline and online A/B tests confirm significant gains in both ranking metrics and user experience, validating the effectiveness and practical value of our approach in real-world search scenarios.</abstract>
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%0 Conference Proceedings
%T An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision
%A Zhang, Zishuai
%A Yu, Sihao
%A Nie, Ying
%A Wang, Junfeng
%A Zheng, Zhiming
%A Yin, Dawei
%A Zhang, Hainan
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%A Xiewenyi
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zhang-etal-2026-efficient-framework
%X The whole-page reranking integrates retrieval results from multiple modalities and is critical for user experience of search engines, yet it requires costly large-scale expert annotations due to the complexity of assessing cross-modal relevances. In this paper, we propose SMAR, a novel whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross modal annotations and distilling intra-modality preferences to align relevance scales across modalities. Specifically, we use pre-trained single-modal rankers to construct candidate pages for limited cross-modal annotation at the page level. The whole-page reranker is then trained on these samples, enforcing consistency with single-modal preferences to preserve intra-modal ranking quality. Experiments on the Qilin and CrossRank datasets demonstrate that SMAR reduces annotation costs by 70-90% while outperforming the fully-annotated reranking baselines. Further offline and online A/B tests confirm significant gains in both ranking metrics and user experience, validating the effectiveness and practical value of our approach in real-world search scenarios.
%U https://aclanthology.org/2026.acl-industry.67/
%P 966-978
Markdown (Informal)
[An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision](https://aclanthology.org/2026.acl-industry.67/) (Zhang et al., ACL 2026)
ACL
- Zishuai Zhang, Sihao Yu, Xiewenyi, Ying Nie, Junfeng Wang, Zhiming Zheng, Dawei Yin, and Hainan Zhang. 2026. An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 966–978, San Diego, California, USA. Association for Computational Linguistics.