DOMFN: A Divergence-Orientated Multi-Modal Fusion Network for Resume Assessment

Abstract

In talent management, resume assessment aims to analyze the quality of a job seeker's resume, which can assist recruiters to discover suitable candidates and benefit job seekers improving resume quality in return. Recent machine learning based methods on large-scale public resume datasets have provided the opportunity for automatic assessment for reducing manual costs. However, most existing approaches are still content-dominated and ignore other valuable information. Inspired by practical resume evaluations that consider both the content and layout, we construct the multi-modalities from resumes but face a new challenge that sometimes the performance of multi-modal fusion is even worse than the best uni-modality. In this paper, we experimentally find that this phenomenon is due to the cross-modal divergence. Therefore, we need to consider when is it appropriate to perform multi-modal fusion? To address this problem, we design an instance-aware fusion method, i.e., Divergence-Orientated Multi-Modal Fusion Network (DOMFN), which can adaptively fuse the uni-modal predictions and multi-modal prediction based on cross-modal divergence. Specifically, DOMFN computes a functional penalty score to measure the divergence of cross-modal predictions. Then, the learned divergence can be used to decide whether to conduct multi-modal fusion and be adopted into an amended loss for reliable training. Consequently, DOMFN rejects multi-modal prediction when the cross-modal divergence is too large, avoiding the overall performance degradation, so as to achieve better performance than uni-modalities. In experiments, qualitative comparison with baselines on real-world dataset demonstrates the superiority and explainability of the proposed DOMFN, e.g., we find a meaningful phenomenon that multi-modal fusion has positive effects for assessing resumes from UI Designer and Enterprise Service positions, whereas affects the assessment of Technology and Product Operation positions.