Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation (2020)

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.

## introduction writing high-quality job postings is the crucial first step to attract and filter the right talents in the recruiting process of human resource management. given job descriptions and basic company information, the key to the job posting is to write job requirements, which requires to specify professional skills properly. both too many or few requirements may lead to negative impacts on talent recruiting. because of the extremely large number of job positions and varieties of professional skills, a lot of companies have to pay much cost in this step to win in the war of talents. to this end, we propose the task of job posting generation (jpg) in this paper, and we cast it as a novel conditional text generation task that generates the job requirement paragraph. exploiting the ubiquitous job posting data, we aim to automatically specify the level of necessary skills and generate fluent job requirements in a data-driven manner, as shown in figure 1 . although the jpg task is of great significance, the complexity of it poses several key challenges: 1) generating job requirements needs to not only produce overall fluent text but also precisely organize the key content like skills and other information, which is very difficult to current neural systems. especially, the long-text to long-text generation easily leads to information missing @xcite . 2) the key points of job descriptions and the skills of job requirements are complex many-tomany relations, which makes the mapping learning very difficult. 3) how to exploit the global information among the heterogeneous relations between basic company information and the professional skills across the whole dataset is of great importance to generate high-quality job requirements. to address these challenges, we focus on the richness and accuracy of skills in generated job requirements and propose a global skill-aware multi-attention (sama) model for jpg task. specifically, we devise a two-pass decoder to generate informative, accurate, and fluent job requirement paragraph. the first-pass decoder is to predict multiple skills according to the job description, which is a multi-label classification task @xcite . the second-pass decoder is to generate a complete text according to the predicted skill labels and the input text. moreover, we build a skill knowledge graph to capture the global information in the whole job posting dataset in addition to the local information provided by the input. through the skill knowledge graph, our model obtains the global prior knowledge to alleviate the misusing of skills. extensive experiments are conducted to evaluate our model on real-world job posting data. the result demonstrates the effectiveness of the proposed method. the main contributions of this paper can be summarized as follows: ## data description we collect a job posting dataset from a famous chinese online recruiting market, across a period of 19 months, ranging from 2019 to 2020. there are 107,616 job postings in total. after removing repetitive and too short job postings, 11,221 records are selected. this dataset is collected from 6 different industry domains. the detailed statistics of the dataset are illustrated in considering the importance of the skills for jpg, we select 2000 records and manually tag the skills in these records. then we train a word-level lstmcrf model @xcite to recognize the skills in the whole dataset. we also keep the basic information, i.e., job position and company scale information, for the reason that they are the critical attributes of job postings that have impacts on the level of skills. in order to capture the global prior knowledge of skills, we construct a skill knowledge graph according to the semantic relations of entities in the job postings. as shown in figure 2 , there are three types of entities, i.e., skill, company scale, and job position. the entities of skills are divided into two types, generic skills (denoted by g) and professional skills (denoted by p), according to the number of occurrences. the relation n.t.m. (needto-master) exists between job position entity and skill entity. besides, the relation in exists between company scale entity and skill entity. for example, jobseeker who is seeking for a programmer position in a company of 10 to 100 people needs to master the professional skill c++, then there exist three triplets, (programmer, n.t.m., c++), ([10, 100], in, c++) and (c++, type, p). 3 approach to tackle the jpg task, we propose a global skill-aware multi-attention model, named sama. figure 3 shows the overall architecture of sama. firstly, considering the importance of skill prediction in jpg, we decompose the probability p (y i |x i , b i ) into a two-stage generation process, including skill prediction and job requirement paragraph generation: secondly, for refining the skills, we leverage the global prior information by the skill knowledge graph g @xmath0, r, e 2 ) where e 1 and e 2 are the sets of head and tail entities and r is the set of relations. given the basic information b i and the skill knowledge graph g s , we obtain a set of skills thirdly, to fuse the local and global information, the probability p (y i |x i , s i , b i ) during the text generation process is calculated as: ## experiments in this section, we conduct experiments to verify the effectiveness of sama. ## related work the related works fall into two categories, human resource management and generation models. ## conclusion in this paper, we proposed the job posting generation (jpg) task and formalized it to a conditional text generation problem. besides, we proposed a novel model, sama, for this task. the merits of sama come from three aspects. firstly, it decomposed the long text generation into two stages, including an mlc task and a multiple skills guided text generation task. secondly, it considered both the local and the global information to generate accurate and rich skills. last but not least, the learned mapping relationships can be applied to various downstream tasks, such as automatic resume, and person-job fit. extensive experiments conducted on real-world job posting data demonstrated the effectiveness and superiority of sama.