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    <abstract>Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per the company information and job description. To this end, we propose a novel task of Job Posting Generation (JPG) which is cast as a conditional text generation problem to generate job requirements according to the job descriptions. To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA. Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels. At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results. The proposed approach is evaluated on real-world job posting data. Experimental results clearly demonstrate the effectiveness of the proposed method.</abstract>
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%0 Conference Proceedings
%T Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation
%A Liu, Liting
%A Liu, Jie
%A Zhang, Wenzheng
%A Chi, Ziming
%A Shi, Wenxuan
%A Huang, Yalou
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-hiring
%X Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per the company information and job description. To this end, we propose a novel task of Job Posting Generation (JPG) which is cast as a conditional text generation problem to generate job requirements according to the job descriptions. To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA. Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels. At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results. The proposed approach is evaluated on real-world job posting data. Experimental results clearly demonstrate the effectiveness of the proposed method.
%R 10.18653/v1/2020.acl-main.281
%U https://aclanthology.org/2020.acl-main.281
%U https://doi.org/10.18653/v1/2020.acl-main.281
%P 3096-3104

Markdown (Informal)

[Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation](https://aclanthology.org/2020.acl-main.281) (Liu et al., ACL 2020)

• Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation (Liu et al., ACL 2020)

ACL

• Liting Liu, Jie Liu, Wenzheng Zhang, Ziming Chi, Wenxuan Shi, and Yalou Huang. 2020. Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3096–3104, Online. Association for Computational Linguistics.