Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network

Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, Ji-Rong Wen

Abstract
Person-job fit has been an important task which aims to automatically match job positions with suitable candidates. Previous methods mainly focus on solving the match task in single-domain setting, which may not work well when labeled data is limited. We study the domain adaptation problem for person-job fit. We first propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively. Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match. Extensive experiment results on a large real-world dataset consisting of six domains have demonstrated the effectiveness of the proposed model, especially when there is not sufficient labeled data.
Anthology ID:
D19-1487
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4810–4820
Language:
URL:
https://aclanthology.org/D19-1487
DOI:
10.18653/v1/D19-1487
Bibkey:
Cite (ACL):
Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, and Ji-Rong Wen. 2019. Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4810–4820, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network (Bian et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/emnlp22-frontmatter/D19-1487.pdf
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