This repository contains the source code for the EMNLP 2019 paper Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network
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, i.e., 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. .
We present the statistics of the linked dataset in the following table:
By using the datasets, you must agree to be bound by the terms of the following licence.
By using the datasets, you must agree to be bound by the terms of the following license.
Then mail to [bianshuqing@ruc.edu.cn] and cc Wayne Xin Zhao via [batmanfly@qq.com] and Yang Song via [songyang@kanzhun.com] and your supervisor, and copy the license in the email. We will send you the datasets by e-mail when approved.
By using the datasets, you must agree to be bound by the terms of the following licence.
Licence agreement
This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:
1. That the dataset comes “AS IS”, without express or implied warranty. Although every effort has been made to ensure accuracy, we do not accept any responsibility for errors or omissions.
2. That you include a reference to the ”BOSS Zhipin“ dataset in any work that makes use of the dataset. For research papers, cite our preferred publication as listed on our References; for other media cite our preferred publication as listed on our website or link to the dataset website.
3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
4. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. That all rights not expressly granted to you are reserved by us (Wayne Xin Zhao, School of Information, Renmin University of China & Yang Song, BOSS Zhipin).
If you use our dataset or useful in your research, please kindly cite our papers.
@inproceedings{shuqing2019job,
title={Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network},
author={Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang and Ji-Rong Wen},
booktitle={EMNLP},
year={2019}
}
- The following people contributed to this work: Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang and Ji-Rong Wen.
- If you have any questions or suggestions with this dataset, please kindly let us know. Our goal is to make the dataset reliable and useful for the community.
- For contact, send email to shuqingbian@gmail.com.