标题： 通过EAR-MP提升产品搜索相关性：CIKM 2025 AnalytiCup的解决方案 显示英文标题

标题： Improving Product Search Relevance with EAR-MP: A Solution for the CIKM 2025 AnalytiCup

摘要： 多语言电子商务搜索由于语言多样性以及用户生成查询中的固有噪声而具有挑战性。 本文记录了我们团队（EAR-MP）用于CIKM 2025 AnalytiCup的解决方案，该方案解决了两个核心任务：查询-类别（QC）相关性和查询-物品（QI）相关性。 我们的方法首先通过将所有文本翻译成英语来规范化多语言数据集，然后通过大量的数据清洗和规范化来减轻噪声。 在模型训练方面，我们基于DeBERTa-v3-large并利用标签平滑、自蒸馏和dropout来提高性能。 此外，我们引入了任务特定的升级，包括针对QC的分层标记注入和针对QI的混合评分机制。 在计算资源受限的情况下，我们的方法取得了有竞争力的结果，在QC上的F1得分为0.8796，在QI上的F1得分为0.8744。 这些发现强调了系统数据预处理和定制化训练策略对于构建稳健且资源高效的多语言相关性系统的重要性。 显示英文摘要

摘要： 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.