通过数据清洗与模型优化，提升多语言电商搜索相关性。
Improving Product Search Relevance with EAR-MP: A Solution for the CIKM 2025 AnalytiCup
- 将多语言文本统一翻译为英文，结合清洗与归一化减少噪声。
- 在有限算力下实现查询-品类与查询-商品的F1分别达0.8796和0.8744。
- 采用分层标记注入与混合打分机制，适配不同任务需求。
多语言电子商务搜索因语言多样性及用户生成查询中的噪声而面临挑战。本文记录了我们团队（EAR-MP）在CIKM 2025 AnalytiCup中解决的两个核心任务：查询-品类（QC）相关性和查询-商品（QI）相关性。方法首先将所有文本统一翻译为英文，并通过大规模数据清洗与归一化降低噪声。模型训练基于DeBERTa-v3-large，结合标签平滑、自蒸馏和丢弃率优化提升性能。针对不同任务引入特定改进：为QC任务设计分层标记注入，为QI任务采用混合评分机制。在计算资源受限条件下，取得QC任务F1为0.8796、QI任务F1为0.8744的竞争力结果。研究强调系统性数据预处理与定制化训练策略对构建高效多语言相关性系统的重要性。
原文摘要 · Abstract (English)
Multilingual e-commerce search is challenging due to linguistic diversity and the noise inherent in user-generated queries. This paper documents the solution employed by our team (EAR-MP) for the CIKM 2025 AnalytiCup, which addresses two core tasks: Query-Category (QC) relevance and Query-Item (QI) relevance. Our approach first normalizes the multilingual dataset by translating all text into English, then mitigates noise through extensive data cleaning and normalization. For model training, we build on DeBERTa-v3-large and improve performance with label smoothing, self-distillation, and dropout. In addition, we introduce task-specific upgrades, including hierarchical token injection for QC and a hybrid scoring mechanism for QI. Under constrained compute, our method achieves competitive results, attaining an F1 score of 0.8796 on QC and 0.8744 on QI. These findings underscore the importance of systematic data preprocessing and tailored training strategies for building robust, resource-efficient multilingual relevance systems.
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