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

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 4810–4820, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 4810 Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network Shuqing Bian1,2, Wayne Xin Zhao ∗1,2, Yang Song3, Tao Zhang3 and Ji-Rong Wen1,2 1School of Information, Renmin University of China 2Beijing Key Laboratory of Big Data Management and Analysis Methods 3BOSS Zhipin {bianshuqing,jrwen}@ruc.edu.cn batmanfly@gmail.com {songyang,kylen.zhang}@kanzhun.com 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, 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. 1 Introduction Recent years have witnessed the rapid growth of online recruitment platforms. According to (Iqbal, 2019), there were 590 million users and 11 million job listings in LinkedIn1 from about over 200 countries and territories all over the world. With the increasing amount of online recruitment data, it has become an essential task that is able to automatically match jobs with suitable candidates, called person-job fit (Qin et al., 2018; Shen et al., 2018). Due to the importance of person-job fit, many efforts have been devoted to improving the match algorithms (Xu et al., 2018; Malinowski et al., 2006; Paparrizos et al., 2011; Lee and Brusilovsky, 2007; Zhang et al., 2014). Among these studies, a typical approach is to casting the ∗Corresponding author 1https://www.linkedin.com task as a supervised text match problem. Given a set of labeled data (i.e., person-job match records), it aims to predict the match label based on the text content of job postings and candidate resumes. More recently, deep learning has shed light on person-job fit methods by learning more effective text representations or text match models (Qin et al., 2018; Zhu et al., 2018; Jiang et al., 2019). Although these methods have achieved signif- icant progress in person-job fit, they rely on a considerable amount of labeled data to learn the match models. When labeled data is insufficient, existing methods may not work well as expected. Therefore, the deficiency in labeled data has raised a significant challenge to the person-job fit algorithms. The availability of labeled data for different job categories is highly varying. A job position from a major category usually attracts hundreds of job applicants, while one from a minor category receives very few applications. Besides, due to the emergence of new positions, data accumulation under these …

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