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        <title>Learning Job Title Representation from Job Description Aggregation Network</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Learning Job Title Representation from Job Description Aggregation Network
%A Laosaengpha, Napat
%A Tativannarat, Thanit
%A Piansaddhayanon, Chawan
%A Rutherford, Attapol
%A Chuangsuwanich, Ekapol
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F laosaengpha-etal-2024-learning
%X 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.
%U https://aclanthology.org/2024.findings-acl.77
%P 1319-1329

Markdown (Informal)

[Learning Job Title Representation from Job Description Aggregation Network](https://aclanthology.org/2024.findings-acl.77) (Laosaengpha et al., Findings 2024)

• Learning Job Title Representation from Job Description Aggregation Network (Laosaengpha et al., Findings 2024)

ACL

• Napat Laosaengpha, Thanit Tativannarat, Chawan Piansaddhayanon, Attapol Rutherford, and Ekapol Chuangsuwanich. 2024. Learning Job Title Representation from Job Description Aggregation Network. In Findings of the Association for Computational Linguistics ACL 2024, pages 1319–1329, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.