Mitigating Language Bias in Cross-Lingual Job Retrieval: A Recruitment Platform Perspective

Napat Laosaengpha, Thanit Tativannarat, Attapol Rutherford, Ekapol Chuangsuwanich

Abstract:

Understanding the textual components of resumes and job
postings is critical for improving job-matching accuracy and
optimizing job search systems in online recruitment platforms. However, existing works primarily focus on analyzing individual components within this information, requiring
multiple specialized tools to analyze each aspect. Such disjointed methods could potentially hinder overall generalizability in recruitment-related text processing. Therefore, we
propose a unified sentence encoder that utilized multi-task
dual-encoder framework for jointly learning multiple component into the unified sentence encoder. The results show that
our method outperforms other state-of-the-art models, despite
its smaller model size. Moreover, we propose a novel metric, Language Bias Kullback–Leibler Divergence (LBKL), to
evaluate language bias in the encoder, demonstrating significant bias reduction and superior cross-lingual performance.