Learning Job Title Representation from Job Description Aggregation Network

Napat Laosaengpha, Thanit Tativannarat, Chawan Piansaddhayanon, Attapol Rutherford, Ekapol Chuangsuwanich

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.