用多语言模型匹配项目与自由职业者，提升跨语种人才检索效率
Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval
- 基于预训练多语言模型构建自定义Transformer，保留简历与项目文本结构
- 通过对比损失在历史数据上训练，显著提升技能匹配相似度捕捉能力
- 适合需要跨语言高效筛选自由职业者的平台或招聘系统使用
在多语言环境下，大规模匹配项目需求与自由职业者仍具挑战。本文提出一种新型神经检索架构，利用预训练多语言语言模型编码项目描述与自由职业者简历。该模型采用定制化Transformer结构，旨在保持原始文本的语义结构。通过历史数据上的对比损失进行训练，实验证明该方法能有效捕捉技能匹配相似性，显著优于传统方法，实现高效精准的人才-项目对齐。
原文摘要 · Abstract (English)
Finding the perfect match between a job proposal and a set of freelancers is not an easy task to perform at scale, especially in multiple languages. In this paper, we propose a novel neural retriever architecture that tackles this problem in a multilingual setting. Our method encodes project descriptions and freelancer profiles by leveraging pre-trained multilingual language models. The latter are used as backbone for a custom transformer architecture that aims to keep the structure of the profiles and project. This model is trained with a contrastive loss on historical data. Thanks to several experiments, we show that this approach effectively captures skill matching similarity and facilitates efficient matching, outperforming traditional methods.
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