Learning Job Title Representation from Job Description Aggregation Network

TL;DR

This paper introduces a novel framework for learning job title representations directly from job descriptions using a description aggregator and bidirectional contrastive loss, outperforming skill-based methods in various settings.

Contribution

It presents an alternative approach that leverages entire job descriptions rather than just skills, enhancing job title representation learning.

Findings

Outperforms skill-based methods in in-domain settings

Effective in out-of-domain scenarios

Utilizes bidirectional contrastive loss for better alignment

Abstract

Learning job title representation is a vital process for developing automatic human resource tools. To do so, existing methods primarily rely on learning the title representation through skills extracted from the job description, neglecting the rich and diverse content within. Thus, we propose an alternative framework for learning job titles through their respective job description (JD) and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss to account for the bidirectional relationship between the job title and its description. We evaluated the performance of our method on both in-domain and out-of-domain settings, achieving a superior performance over the skill-based approach.