@inproceedings{laosaengpha-etal-2024-learning,
title = "Learning Job Title Representation from Job Description Aggregation Network",
author = "Laosaengpha, Napat and
Tativannarat, Thanit and
Piansaddhayanon, Chawan and
Rutherford, Attapol and
Chuangsuwanich, Ekapol",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.77",
doi = "10.18653/v1/2024.findings-acl.77",
pages = "1319--1329",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="laosaengpha-etal-2024-learning">
<titleInfo>
<title>Learning Job Title Representation from Job Description Aggregation Network</title>
</titleInfo>
<name type="personal">
<namePart type="given">Napat</namePart>
<namePart type="family">Laosaengpha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thanit</namePart>
<namePart type="family">Tativannarat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chawan</namePart>
<namePart type="family">Piansaddhayanon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Attapol</namePart>
<namePart type="family">Rutherford</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekapol</namePart>
<namePart type="family">Chuangsuwanich</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lun-Wei</namePart>
<namePart type="family">Ku</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andre</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivek</namePart>
<namePart type="family">Srikumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bangkok, Thailand</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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>
<identifier type="citekey">laosaengpha-etal-2024-learning</identifier>
<identifier type="doi">10.18653/v1/2024.findings-acl.77</identifier>
<location>
<url>https://aclanthology.org/2024.findings-acl.77</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>1319</start>
<end>1329</end>
</extent>
</part>
</mods>
</modsCollection>
%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
%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.
%R 10.18653/v1/2024.findings-acl.77
%U https://aclanthology.org/2024.findings-acl.77
%U https://doi.org/10.18653/v1/2024.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)
ACL