# HR Management Big Data Mining Based on Computational Intelligence and Deep Learning

Authors: Genliang Zhao; Zhe Xue
Journal: International Journal of Antennas and Propagation
 (2021)

---

## Abstract

Decomposing the structure of a large number of existing posts through data mining will greatly improve the effect of enterprise human resource structure optimization. To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. Then an encoder-decoder LSTM is proposed to implement job requirement generation, and a competency-aware attention mechanism and a replication mechanism are proposed to guide the generation process to ensure that the generated job requirement descriptions comprehensively cover the relevant and representative competency and job skill requirements. A competency-aware strategy gradient training algorithm is then proposed to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants compared to state-of-the-art benchmarks.


---

## Body

## 1. Introduction

In order to achieve long-term development, enterprises need to put the advantages of HRs to better use. The modern human capital theory consensus points out that the most valuable asset of an enterprise and the trump card that can win long-term profit in the competitive market and gain maximum benefit for the enterprise is human capital [1]. If an enterprise wants to achieve long-term sustainable development and formulate a more permanent development strategy, it must take precise and detailed HR planning as the first priority, and especially in the current situation of rising human resource costs, only the accurate deployment of human resource costs in advance can reduce costs [2]. Only by making the HR cost planning precise and detailed in advance can, we reduce the cost of manpower and shift to a more efficient cost allocation model [3].Even if the labor productivity of the best employees in the same position is much higher than that of the average or poor employees, the best employees in the same position should not be taken as the standard for staffing [4]. Only by systematically screening and judging the skills, experience, and level of different employees as well as the needs of the position can we find the most suitable employees for the position so as to achieve the best overall organizational effectiveness [5, 6]. However, it is worth noting that the process of HR allocation is not a simple selection process but only relies on scientific methods to achieve the best results of the system.In the use of HR computing intelligence, enterprises are able to access all the contents that are closely related to HR [7]. The relevant data and information on the practical application, on the one hand, and easy to grasp specific information of enterprise development, on the other hand, can provide a reference for the enterprise to make the corresponding management decisions. When data mining technology is applied to HR management, the main content can be divided into three categories: The first category is real-time data. This type of data is mainly reflected in the personnel roster, including individual and organizational levels, where the individual level contains the number of personnel, personnel structure, work experience, age structure, education structure, skills and specialties, certification structure, and family background [8]. The organization level contains six modules, including HR management, HR strategy management, payroll, and performance management. The second category is dynamic data. This part of data is usually reflected in data reports, such as labor cost tables, and so on [9]. In the management of such data, statistical calculations and tracking records are required. The third category is integrated data. It mainly refers to the information in the form of designed questionnaires and so on and after integration and analysis, such as employee satisfaction.There are a limited number of personnel at different levels, and too many or too few will affect the stable operation of the company [10, 11]. Therefore, the ratio of supervisors to employees should be kept within a reasonable range. At the same time, in HR management, different management methods are implemented for a given number of employees, and there are differences in the management effect. At the same time, the same management style for different quality and able employees will also make a difference in management efficiency. Therefore, it is crucial for enterprises to adopt scientific and effective management methods according to different information in HR management, while using traditional management methods, it is difficult to realize the use and effective mastering of corresponding information care. In contrast, the management with the assistance of data mining technology in the new era can improve the effect of carrying out the relevant work [12]. For example, if a company controls the proportion of employees who are responsible for the corresponding functions, by analyzing information such as the work capacity of the personnel concerned and the number of people served, it is possible to quickly determine whether the staffing should be increased, maintained, or reduced and improve the rational use of HR [13].Further, this paper proposes an end-to-end competency-aware neural job requirement generation framework to automate the generation of job requirements, and the prediction based on competency themes enables the prediction of skill words in job requirements. A neural theme model is first designed to explore various competency and skill-related information from real-world HR data. Then an encoder-decoder recurrent neural network is proposed to implement job requirement generation, and a competency-aware attention mechanism and a replication mechanism are proposed to guide the generation process to ensure that the generated job requirement descriptions comprehensively cover the relevant and representative competencies and skill requirements of the job. A competency-aware strategy gradient training algorithm is then proposed to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants compared to state-of-the-art benchmarks. Thus, the proposed framework can be effectively applied to talent attraction scenarios in HR services.


## 2. Related Work

Following the commonly used definition, computational intelligence refers to the nontrivial process of identifying novel, potentially useful, and valid patterns in data [9, 14]. During this period, there is a wide range of data mining application areas and corresponding research fields, which also include the field of business management, as well as well-established subfields such as customer management, manufacturing management, or financial management [14].Recently, these enterprise application domains seem to be complemented by HRM. In the last few years, an increasing number of research contributions aim to support the practical adoption of data mining in HRM. Contributions are various activities and processes of HRM, such as selecting employees or predicting employee turnover [15], to determine the competencies of employees in development, or predicting and evaluating employee performance in performance management [16–18]. To provide these functions, a whole range of data mining methods such as classification trees [19], clustering [20], association analysis [21], support vector machines [22], or neural networks [12, 23] are used, while system improvements and customizations [24] are also presented. In short, browsing the literature gives the impression of a flourishing new field of data mining research that fits the specific requirements of the HR field and is therefore very useful for HR practice.However, the large number of relevant contributions and different results complicate the overview of the current state of research. Therefore, this thesis aims to design a rational architecture for HRM to be effectively applied to talent attraction scenarios in HR services.


## 3. Data-Mining-Based Multifactor HR Requirements

### 3.1. Data Mining

Data mining is also the effective use of all mathematical algorithms to discover potential patterns from the resulting information. Therefore, it can also be said that the process of finding out the inner laws of the company’s HR needs and other influencing elements that interact with each other is to make demand forecasts for the process of data mining in the context of both internal and external effects of the company [25].Machine learning uses statistics to uncover general patterns that exist in various types of input data and builds training models based on them to predict new input outcomes. For example, support vector machines are based on statistical learning theory, which can reduce structural risk and have the advantages of being theoretically adequate and easy to operate [26].Initially, support vector machines were proposed in the context of data classification, but the role of kernel functions and support vectors in support vector machines led to the extension of the problem to the field of regression analysis, giving birth to the problem of vector regression machines, also known as support vector regression. The minimum deviation of all sampling points can be obtained in the sample space, and thus, the effect of nonlinear regression in the original space can be derived. The SVR-based features can explore the outstanding performance in the sample data, so it is very useful for enterprises to predict HRs.The equation defining the regression function is as follows:(1)fx=w×ϕx+b.In the high-dimensional eigenspace, the SVR represents the input quantity better by means of the kernel function, while the penalty coefficientC and the relaxation variable ε are introduced together to optimize the daily function as follows:(2)min12ω2+C∑ξi+ξj+.The calculation of the extremal point is achieved mainly by means of the Lagrangian function.


### 3.2. Variable Weight Support Vector Regression Machines

When forecasting the demand for HRs, it is necessary to effectively enter the historical data of the time series, which is characterized by a gradual decline with time regression [27, 28]. In the process of regression, the regression error between the earlier data and the new data is almost zero. The weight of the slack variables in the traditional SVR model is the same, and the large variance samples are absolutely dominant in the regression super flat, which allows the regression distortion to appear. With the help of the weight coefficient vector λ1,λ2,…,λn, an identical small penalty strength is taken for all samples, and the importance of early and recent data in the sample series is effectively distinguished so that the regression effects in each sample are scientifically integrated.The weighting coefficients can be indexed, that is,(3)λi=kei−N,where N is the total number of years of historical data.


## 3.1. Data Mining

Data mining is also the effective use of all mathematical algorithms to discover potential patterns from the resulting information. Therefore, it can also be said that the process of finding out the inner laws of the company’s HR needs and other influencing elements that interact with each other is to make demand forecasts for the process of data mining in the context of both internal and external effects of the company [25].Machine learning uses statistics to uncover general patterns that exist in various types of input data and builds training models based on them to predict new input outcomes. For example, support vector machines are based on statistical learning theory, which can reduce structural risk and have the advantages of being theoretically adequate and easy to operate [26].Initially, support vector machines were proposed in the context of data classification, but the role of kernel functions and support vectors in support vector machines led to the extension of the problem to the field of regression analysis, giving birth to the problem of vector regression machines, also known as support vector regression. The minimum deviation of all sampling points can be obtained in the sample space, and thus, the effect of nonlinear regression in the original space can be derived. The SVR-based features can explore the outstanding performance in the sample data, so it is very useful for enterprises to predict HRs.The equation defining the regression function is as follows:(1)fx=w×ϕx+b.In the high-dimensional eigenspace, the SVR represents the input quantity better by means of the kernel function, while the penalty coefficientC and the relaxation variable ε are introduced together to optimize the daily function as follows:(2)min12ω2+C∑ξi+ξj+.The calculation of the extremal point is achieved mainly by means of the Lagrangian function.


## 3.2. Variable Weight Support Vector Regression Machines

When forecasting the demand for HRs, it is necessary to effectively enter the historical data of the time series, which is characterized by a gradual decline with time regression [27, 28]. In the process of regression, the regression error between the earlier data and the new data is almost zero. The weight of the slack variables in the traditional SVR model is the same, and the large variance samples are absolutely dominant in the regression super flat, which allows the regression distortion to appear. With the help of the weight coefficient vector λ1,λ2,…,λn, an identical small penalty strength is taken for all samples, and the importance of early and recent data in the sample series is effectively distinguished so that the regression effects in each sample are scientifically integrated.The weighting coefficients can be indexed, that is,(3)λi=kei−N,where N is the total number of years of historical data.


## 4. HR Demand Forecasting Case

Taking a company dealing with automobiles as an example, if the HR demand of the company is analyzed, the results generated under this method are tested for prediction. Based on the analysis of correlation, the relevant factors are reasonably selected, and the total output value, total profit, sales situation, and model number are used as the core elements to forecast the HR demand [13, 21].


### 4.1. Preprocessing of Data

If the difference between the numerical magnitudes of the key factors is very obvious, it will cause a serious impact on the serial variance gap of each factor, and if the use is carried out directly, the influences with a large variance will cause a direct impact on the regression results, so it is necessary to preprocess all the data [28]. When processing each group of data, the z-score method can be used, and its formula is as follows:(4)yi=xi−x¯δλ,where x is the original data, y is the predicted value, and δλ is the distribution factor. After preprocessing, the approximate numerical magnitude of all core factors was effectively obtained.


### 4.2. Forecasting Variable-Weight SVR HR Requirements

The kernel function is filled by a Gaussian function as follows:(5)Kxi,xj=exp−1δ2xi−xj2.The experimental findings are carefully analyzed, while the experience accumulated over the years is effectively combined, and the kernel width is set toδ2=2, so that the high-dimensional nonlinearity of the data is better represented. When the penalty factor C = 100, the penalty factor can be avoided, which results in deterioration of performance and generalization of the data. When the base of the relaxation variable in the model is set to 0.01, the accuracy of the data points is very high and the number of support vectors in the training model is minimal, which results in a better extrapolation of the model. In order to enable the prediction accuracy of the method, five years of historical data from 2015 to 2019 were synthesized into a training set [26, 29], which allowed the regression model to be created in a reasonable manner. The actual situation of the company’s HRs in 2019 has met the company’s strategy implementation needs to the greatest extent, which fully demonstrates the effectiveness of the forecasting method. This is a good indication of the effectiveness of the method. Using this method to forecast the company’s HR demand in 2020, the six years of historical data from 2015 to 2020 were combined into a training set, and all key factors for 2020 were entered into the SVR model, resulting in an HR demand of 5,963 people in 2020, with a shortfall of more than 300 people.


## 4.1. Preprocessing of Data

If the difference between the numerical magnitudes of the key factors is very obvious, it will cause a serious impact on the serial variance gap of each factor, and if the use is carried out directly, the influences with a large variance will cause a direct impact on the regression results, so it is necessary to preprocess all the data [28]. When processing each group of data, the z-score method can be used, and its formula is as follows:(4)yi=xi−x¯δλ,where x is the original data, y is the predicted value, and δλ is the distribution factor. After preprocessing, the approximate numerical magnitude of all core factors was effectively obtained.


## 4.2. Forecasting Variable-Weight SVR HR Requirements

The kernel function is filled by a Gaussian function as follows:(5)Kxi,xj=exp−1δ2xi−xj2.The experimental findings are carefully analyzed, while the experience accumulated over the years is effectively combined, and the kernel width is set toδ2=2, so that the high-dimensional nonlinearity of the data is better represented. When the penalty factor C = 100, the penalty factor can be avoided, which results in deterioration of performance and generalization of the data. When the base of the relaxation variable in the model is set to 0.01, the accuracy of the data points is very high and the number of support vectors in the training model is minimal, which results in a better extrapolation of the model. In order to enable the prediction accuracy of the method, five years of historical data from 2015 to 2019 were synthesized into a training set [26, 29], which allowed the regression model to be created in a reasonable manner. The actual situation of the company’s HRs in 2019 has met the company’s strategy implementation needs to the greatest extent, which fully demonstrates the effectiveness of the forecasting method. This is a good indication of the effectiveness of the method. Using this method to forecast the company’s HR demand in 2020, the six years of historical data from 2015 to 2020 were combined into a training set, and all key factors for 2020 were entered into the SVR model, resulting in an HR demand of 5,963 people in 2020, with a shortfall of more than 300 people.


## 5. The Proposed Framework

### 5.1. Problem Definition

The goal of this paper is to automate the generation of job requirement descriptions. Given a setC of job requirement documents for c different jobs, that is, c=Ci=Xi,Yii=1c, where Xi is the job duties, which describe the duties of the i-th job, and Yi is the job requirements, which describe the various competency needs of the job. Specifically, for each job responsibility Xi, it is assumed to contain Md words, that is, Xi=x1,x2,…,xMd requirements typically contain multiple sentences to describe different competency requirements, so Yi each job requirement is represented as Yi=y1,y2,…,yN, where yj is the j-th sentence. For example, Figure 1 contains five job requirement statements, that is, N = 5, which correspond to the introduction of education, programming, machine learning, audio processing, and teamwork; the different colors in Figure 1 represent different neurons.Figure 1
Schematic diagram of the framework of this paper (without CAPGTA).In addition, it is assumed that eachyj contains Mjc words, that is, yj=yj,1,yj,2,…,yj,Mjc. In order to analyze the fine-grained competency requirements of each job, the idea of the paper is followed here to train a neural model to extract the skill words in each job requirement. Based on the annotation of these words, a list of competency words corresponding to each yj can be generated, that is, sj=sj,1,sj,2,…,sj,Mjs. Based on this idea, the following job requirement description generation problem is defined in this section.Problem definition: given a set of HR text blocksC. Each ci∈c contains a job responsibility Xi and a job requirement Yi. The goal of job requirement description generation is to learn a model M whose smooth and reasonable job requirements Ynew can be generated when a new job responsibility Xnew is given.The proposed automatic job requirement generation framework (Cajon) based on skill prediction contains three main components: capability-aware under neural topic model (CANTM), the neural model for job requirement generation under ability perception (CANJRG), and the policy gradient training algorithm under ability perception (CAPGTA). Figure1 shows a schematic diagram of the framework without the CAPGTA training algorithm.


### 5.2. CANTM

This subsection proposes a novel CANTM for mining potential competence topics in job responsibilities and job requirements, as shown in Figure2. Next, the generation process and the inference process in CANTM are described separately. CANTM generation process: in order to model the potential semantics in job responsibilities and job requirements, we assume that there exists two topic spaces with the number of potential topics of Kd and Ks. Each topic is divided into Kjd and Kjs, respectively.Figure 2
Schematic diagrams of CANTM probability diagram (the solid line represents the generation process, and the dashed line represents the variational inference process).Word distributionsβjd and βjs can be expressed as(6)βjd=softmaxvd⋅tdj⊤,βjs=softmaxvs⋅tsj⊤,where td∈RKd×H and tS∈RKS×H′ are topic-based parameters, respectively, and vd∈RVd×H and vS∈RVS×H′ are word-based parameters, respectively, all of which will be learned during the training process. The other Vd and VS are the word list sizes for job responsibilities and job requirements, respectively. And only the list of competency words si is considered here as data input for the job requirement part of CANTM, which can reduce input noise and improve the performance of learning potential competency topics in job requirements.Similar to the LAD topic model [30], it is assumed here that each job duty Xi and list of competency words si in the job requirements Yi have topic vectors θd and θS, respectively, where θd∈RKd and θS∈RKs. Here, θd and θS are generated based on Gaussian softmax, respectively. Specifically, the generation process for post Xi is as follows:Sampling hidden variableszd∼Nμd,σd2:(7)θd=softmaxfθdzd.For theL-th word in Xi: sampled word xl∼θd⋅βd, where μd and σd are a priori parameter and fθd⋅ is a neuron activated by a nonlinear function.The difference is that for the generation process of competency word listsSi in job requirements Yi, usually, only one competency topic is designed. Based on this, the generation process is as follows:Sampling hidden variableszs∼Nμd,σds:(8)θs=softmaxfθszs.The probability of the wordsj in the j-th sentence can be expressed as(9)psj|θs,βs=θs⋅∏k=1Mjsβ∗,sj,ks,where μs and σs are a priori parameters and β∗,sj,ks represents the column vector of ability words in βs. In this paper, an end-to-end competency-aware neural job requirement generation framework is proposed to automate the generation of job requirements, and the prediction of skill words in job requirements can be achieved based on the prediction of competency themes. A neural topic model is first designed; then an encoder-decoder LSTM is proposed to implement a job requirement generation, followed by a competency-aware policy gradient-based training algorithm to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants in comparison with state-of-the-art benchmarks.In addition, in order to model the strong correlation between each job positionXi and the competency term Si in the job requirements Yi, the following mapping relationship is assumed here for the a priori parameters of their potential topics.μs=Wμμd,logσs=Wσlogσd CANTM Inference Process: the edge likelihood [31] of the CANTM-based generation process is as follows:(10)pXi,Si|μd,μs,σd,σs,βd,βs=∫pθd|μd,σd2∫k=1Mdpxk|θd,βddθd⋅∫pθs|μs,σs2∏j=1Npsj|θs,βsdθs.The neural variational method is used here to approximate the posterior distribution onθd and θs. Based on equation (10), the variational lower bound for the log-likelihood is as follows:(11)ℒ=Eqθd∑k=1Mlogpxk|θd,βd−DKLqθdpθd|μd,σd+Eqθs∑j=1Mlogpsj|θs,βs−DKLqθspθs|μs,σs,where qθs and qθd are estimates of the variance distribution of the true posterior pθd|Xi,Si and pθS|Xi,Si, respectively. DKL represents the Kullback–Leibler divergence [5, 32]. The proof is derived as follows:(12)ℒ=log∫pθd|μd,σd2∏k=1Mdpxk|θd,βddθd+log∫pθs|μs,σs2∏j=1Npsj|θs,βsdθs=log∫qθdqθdpθd|μd,σd2∏k=1Mdpxk|θd,βddθd+log∫qθsqθspθs|μs,σs2∏j=1Npsj|θs,βsdθs≥∫qθdlogpθd|μd,σd2∏k=1Mdpxk|θd,βddθd+∫qθslogpθs|μs,σs2∏j=1Npsj|θs,βsdθs−∫qθdlogqθddθd−∫qθslogqθsdθs=Eqθd∑k=1Mdlogpxk|θd,βd−DKLqθdpθd|μd,σd2+Eqθr∑j=1Nlogpsj|θs,βs−DKLqθspθs|μs,σs2.We generate the variance parametersμdXi,Si,logσdXi,Si,μsXi,Si, and logσSXi,Si here based on the idea of the paper to estimate μd, σd, μs, and σs through input Xi. This allows the CANTM model to explore potential competency thematic representations through job duties only θd and θs. Therefore, an inferential network based on the observed job duties Xi is introduced here, combined with equation (12) to generate the above variance parameters as follows:(13)μdXi,Si=fμdfedXibow,μsXi,Si=WμμdXi,Si,logσdXi,Si=fσdfedXibow,logσsXi,Si=WσlogσdXi,Si,where Xibow is the bag-of-words vector of Xi, fed⋅ is a neuron activated by a nonlinear function, and fμd⋅ and fσd⋅ are linear neural perceptual functions.Based on this, the following loss function can be directly minimized for each set of instancesXi,Si during the training process:(14)ℒCANTM=−∑k=1Mdlogθd⋅β∗,xkd+DKLqθdpθd.Therefore, all parameters in CANTM can be inferred, and the potential competency themes involved in each position can be further explored.


### 5.3. CANJRG

After learning about the potential competency themes through CANTM, this subsection describes how to use the encoder-decoder neural model to generate job requirements. As shown in Figure3, it contains two main components, including a sequence encoder that extracts semantic information from the input job responsibilities Xi, and a sequence decoder under competency awareness, which can generate each word in the job requirements Yi by the guidance of potential competency themes.Figure 3
Schematic diagrams of the attention mechanism under ability perception: (a) calculation of attention mechanism of ability perceptionμjt and (b) calculation of attention mechanism of ability perception μj,kc.Sequence encoder: first use an embedding layer to find the embedding vectorekd for each word xk in Xi and then use a Bi-LSTM [5, 33] to encode the sequence e1d,e2d,…,eMdd: hkd⟶=LSTMekd,hk−1d⟶,hkd¯=LSTMekd,hk+1d←, where ekd=Wedxk is the word vector of xk and the LSTM is a unidirectional LSTM network. Finally, hkd=hkd⟶;hkd← is used to represent the final hidden vector of the xk sequence encoder.Competency-aware sequence decoder: the following describes how to construct a decoder to generate each word in job requirementYi. In the generation process, the competency topic tj is first estimated for each sentence yj in Yi, and then each word is predicted the following probability yj,k:(15)pyj,k|Xi=pyj,k|y<j,yj,<k,ℋ,θs,tj,where y<j represents the sequence y1,y2,…,yj−1,yj,<k and represents yj,1,yj,2,…,yj,k−1andℋ=h1d,h2d,…,hMdd represents the implicit state of all sequence encoders. θs is Yi; the implicit capability topic vector learned through CANTM, and tj∈1,Ks is the topic label for each utterance yj.Specifically, the sequence decoder under capability awareness is constructed based on two one-way LSTMs [34]. hjt and hj,kc represent the implicit status of the competent topics tj and words yj,k computed by the LSTM, respectively.(16)hjt=LSTMej−1t;θs;hj−1,Mj−1cc,hj−1thj,kc=LSTMej,k−1c;θs;hjt,hj,k−1c,ejt=Wee′tj,ej,kc=Wecyj,k,where ejt and ej,kc is the embedded representations of tj and yj,k. Mjc is the length of statement yj. Also, implicit states h0t and h1,0c are initialized by hMdd, and hj,0c is initialized by hj−1,Mj−1cc. In addition, two capability-aware attention mechanisms are designed here to capture contextual features from H to enhance the performance of the generation process as follows:(17)αl,jt=expgl,jt∑p=1Nexpgp,jt,αl,j,kc=expgl,j,kc∑p=1Mjcexpgp,j,kc,gl,jt=vα1⊤tanhWα1hld;hjt;θs+bα′,gl,j,kc=vαc⊤tanhWαchld;hj,kc;θs+bαc.The capability-aware context vectorsujt and uj,kc can then be calculated by the following equations:(18)ujt=∑l=1Nαl,jthld,uj,kc=∑l=1Mjcαl,j,kchld.The ability theme labels can then be predicted.tj and yj for each word in yj,k as follows:(19)ptj|t<j,ℋ,θs=softmaxWthjt;ujt;θs+bt,pyj,k|y<j,yj,<k,ℋ,θs,tj=softmaxWchj,kc;uj,kc;θs+bc.In addition, an ability-aware replication mechanism is designed such that the proposed decoder can directly replicate the words in the ability vocabulary. Specifically, a generation probability is defined here whenyj generates the k-th word: yj,k; pgen∈0,1.The probability distribution of the predicted words based on the ability word list can then be updated with the following equation:(20)pyj,k|y<j,yj,<k,ℋ,θs,tj=pgensoftmaxWchj,kc;uj,kc;θs+bc+1−pgenβstj,where βstj is the word distribution in topic.Finally, in the heterogeneous model, for each group of training instancesXi,Yi, the parameters in the model are learned by minimizing the following cross-entropy loss function:(21)ℒCTL=−∑j=1Nlogptj|Xi,θs,ℒGJR=−∑j=1N∑k=1Mjslogpyj,k|Xi,θs.


### 5.4. Capability-Aware Policy Gradient Training Algorithm (CAPGTA)

Before introducing CAPGTA, a basic end-to-end training approach will be shown to learn all the parameters in the above two models. Specifically, because of the CANTM neural variation, the loss functionsℒCANTM, ℒCTL and ℒGJR, can be trained jointly at the same time.ℒ∗=ℒGJR+λ1ℒCANTM+λ2ℒCTL, where λ1 and λ2 are hypermastigote to balance each model. The teacher forcing algorithm is used in the training process, that is, the previous real word yj,k−1 is used in the training to calculate hjt and hj,kc. For the ability topic tj−1, the following is used for generation:(22)tj−1=argmaxlθsl∏k=1Mj−1sβ∗,sj−1,ksll=1Ks.And the predicted values are used as input in the testing sessions.Direct minimizationℒ∗ does not always generate the best job requirements because it does not directly optimize discrete assessment metrics such as ROUGE and BLKJ [35]. In addition, it is desired here that the accuracy of the competencies involved in the generated job requirements can be optimized more intuitively, so that the rationality and validity of the generated results can be better ensured.Some recent reinforcement learning techniques can be used to solve this nondifferentiable task metric problem. Here, the combination of CANTM and CANJRG can be considered as an agent [30, 36], which interacts with the environment, that is, the training instances. Given an input job duty X, strategy pol−icypθyj,kX,y^<j,yj<k is determined by the parameter 0 of the intelligence for each action, that is, predicting the next word based on the current state. Until the end position (EOS) of the sequence of job requirements is generated, a reward will be observed. The goal of the whole training is to learn the strategy by minimizing the negative expected reward of(23)ℒRL=−EY^∼pθY^|XrY^.Based on reinforcement learning algorithm, it obtains that(24)∇θℒRL=−EY^∼pθY^|XrY^∇θlogpθY^|X.It is possible to use a simple Monte–Carlo samplingY^ based strategy pθ as follows:(25)∇θℒRL≈−rY^∇θlogpθY^|X=−rY^∇θ∑j=1N∑k=1Mjclogpθyj,kX,y^<j,yj<k=−rY^∇θ∑j=1Nlogpθt^j|X,t^<j+∑k=1Mjclogpθyj,kX,y^<j,yj<k,where t^1,…,t^N is a Monte–Carlo [37] sample of the capability label. pθt^j|X,t^<j and pθyj,kX,y^<j,yj<k are calculated from equations (23) and (24), respectively.(26)∇θℒRL≈rY^g−rY^∇θ∑j=1Nlogpθt^j|X,t^<j+∑k=1Mjclogpθyj,kX,y^<j,yj<k.As mentioned earlier, here, it is desired to directly optimize the accuracy of the competencies in the generated job requirements. Therefore, we use theF1 values [38] of the generated skill terms as a reward function, that is,(27)rcapabilityY^=2CardS^∩SCardS^+CardS,where S is the set of skill words in the actual job requirements and S^ is the set of skill words in Y^, representing the set size. The ROUGE-1 score is also incorporated into the reward function, which is used to measure statistical information based on the longest common subsequence between the actual and model-generated job requirements. This allows a direct optimization of the similarity of the sentence hierarchy to authenticity, which helps improve the fluency of the generated text. The reward function can then be set to(28)rY^=λ3rcapabilityY^+Rouge−1Y^,Y.Finally,ℒ∗ and ℒRL are used jointly to obtain the overall learning objective function as follows:(29)ℒ=1−γℒ∗+γℒRL,where γ is the dynamic hypernatremia during the training process. It is first set to 0 for a period of training alone ℒ∗, and then the value is gradually increased.


## 5.1. Problem Definition

The goal of this paper is to automate the generation of job requirement descriptions. Given a setC of job requirement documents for c different jobs, that is, c=Ci=Xi,Yii=1c, where Xi is the job duties, which describe the duties of the i-th job, and Yi is the job requirements, which describe the various competency needs of the job. Specifically, for each job responsibility Xi, it is assumed to contain Md words, that is, Xi=x1,x2,…,xMd requirements typically contain multiple sentences to describe different competency requirements, so Yi each job requirement is represented as Yi=y1,y2,…,yN, where yj is the j-th sentence. For example, Figure 1 contains five job requirement statements, that is, N = 5, which correspond to the introduction of education, programming, machine learning, audio processing, and teamwork; the different colors in Figure 1 represent different neurons.Figure 1
Schematic diagram of the framework of this paper (without CAPGTA).In addition, it is assumed that eachyj contains Mjc words, that is, yj=yj,1,yj,2,…,yj,Mjc. In order to analyze the fine-grained competency requirements of each job, the idea of the paper is followed here to train a neural model to extract the skill words in each job requirement. Based on the annotation of these words, a list of competency words corresponding to each yj can be generated, that is, sj=sj,1,sj,2,…,sj,Mjs. Based on this idea, the following job requirement description generation problem is defined in this section.Problem definition: given a set of HR text blocksC. Each ci∈c contains a job responsibility Xi and a job requirement Yi. The goal of job requirement description generation is to learn a model M whose smooth and reasonable job requirements Ynew can be generated when a new job responsibility Xnew is given.The proposed automatic job requirement generation framework (Cajon) based on skill prediction contains three main components: capability-aware under neural topic model (CANTM), the neural model for job requirement generation under ability perception (CANJRG), and the policy gradient training algorithm under ability perception (CAPGTA). Figure1 shows a schematic diagram of the framework without the CAPGTA training algorithm.


## 5.2. CANTM

This subsection proposes a novel CANTM for mining potential competence topics in job responsibilities and job requirements, as shown in Figure2. Next, the generation process and the inference process in CANTM are described separately. CANTM generation process: in order to model the potential semantics in job responsibilities and job requirements, we assume that there exists two topic spaces with the number of potential topics of Kd and Ks. Each topic is divided into Kjd and Kjs, respectively.Figure 2
Schematic diagrams of CANTM probability diagram (the solid line represents the generation process, and the dashed line represents the variational inference process).Word distributionsβjd and βjs can be expressed as(6)βjd=softmaxvd⋅tdj⊤,βjs=softmaxvs⋅tsj⊤,where td∈RKd×H and tS∈RKS×H′ are topic-based parameters, respectively, and vd∈RVd×H and vS∈RVS×H′ are word-based parameters, respectively, all of which will be learned during the training process. The other Vd and VS are the word list sizes for job responsibilities and job requirements, respectively. And only the list of competency words si is considered here as data input for the job requirement part of CANTM, which can reduce input noise and improve the performance of learning potential competency topics in job requirements.Similar to the LAD topic model [30], it is assumed here that each job duty Xi and list of competency words si in the job requirements Yi have topic vectors θd and θS, respectively, where θd∈RKd and θS∈RKs. Here, θd and θS are generated based on Gaussian softmax, respectively. Specifically, the generation process for post Xi is as follows:Sampling hidden variableszd∼Nμd,σd2:(7)θd=softmaxfθdzd.For theL-th word in Xi: sampled word xl∼θd⋅βd, where μd and σd are a priori parameter and fθd⋅ is a neuron activated by a nonlinear function.The difference is that for the generation process of competency word listsSi in job requirements Yi, usually, only one competency topic is designed. Based on this, the generation process is as follows:Sampling hidden variableszs∼Nμd,σds:(8)θs=softmaxfθszs.The probability of the wordsj in the j-th sentence can be expressed as(9)psj|θs,βs=θs⋅∏k=1Mjsβ∗,sj,ks,where μs and σs are a priori parameters and β∗,sj,ks represents the column vector of ability words in βs. In this paper, an end-to-end competency-aware neural job requirement generation framework is proposed to automate the generation of job requirements, and the prediction of skill words in job requirements can be achieved based on the prediction of competency themes. A neural topic model is first designed; then an encoder-decoder LSTM is proposed to implement a job requirement generation, followed by a competency-aware policy gradient-based training algorithm to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants in comparison with state-of-the-art benchmarks.In addition, in order to model the strong correlation between each job positionXi and the competency term Si in the job requirements Yi, the following mapping relationship is assumed here for the a priori parameters of their potential topics.μs=Wμμd,logσs=Wσlogσd CANTM Inference Process: the edge likelihood [31] of the CANTM-based generation process is as follows:(10)pXi,Si|μd,μs,σd,σs,βd,βs=∫pθd|μd,σd2∫k=1Mdpxk|θd,βddθd⋅∫pθs|μs,σs2∏j=1Npsj|θs,βsdθs.The neural variational method is used here to approximate the posterior distribution onθd and θs. Based on equation (10), the variational lower bound for the log-likelihood is as follows:(11)ℒ=Eqθd∑k=1Mlogpxk|θd,βd−DKLqθdpθd|μd,σd+Eqθs∑j=1Mlogpsj|θs,βs−DKLqθspθs|μs,σs,where qθs and qθd are estimates of the variance distribution of the true posterior pθd|Xi,Si and pθS|Xi,Si, respectively. DKL represents the Kullback–Leibler divergence [5, 32]. The proof is derived as follows:(12)ℒ=log∫pθd|μd,σd2∏k=1Mdpxk|θd,βddθd+log∫pθs|μs,σs2∏j=1Npsj|θs,βsdθs=log∫qθdqθdpθd|μd,σd2∏k=1Mdpxk|θd,βddθd+log∫qθsqθspθs|μs,σs2∏j=1Npsj|θs,βsdθs≥∫qθdlogpθd|μd,σd2∏k=1Mdpxk|θd,βddθd+∫qθslogpθs|μs,σs2∏j=1Npsj|θs,βsdθs−∫qθdlogqθddθd−∫qθslogqθsdθs=Eqθd∑k=1Mdlogpxk|θd,βd−DKLqθdpθd|μd,σd2+Eqθr∑j=1Nlogpsj|θs,βs−DKLqθspθs|μs,σs2.We generate the variance parametersμdXi,Si,logσdXi,Si,μsXi,Si, and logσSXi,Si here based on the idea of the paper to estimate μd, σd, μs, and σs through input Xi. This allows the CANTM model to explore potential competency thematic representations through job duties only θd and θs. Therefore, an inferential network based on the observed job duties Xi is introduced here, combined with equation (12) to generate the above variance parameters as follows:(13)μdXi,Si=fμdfedXibow,μsXi,Si=WμμdXi,Si,logσdXi,Si=fσdfedXibow,logσsXi,Si=WσlogσdXi,Si,where Xibow is the bag-of-words vector of Xi, fed⋅ is a neuron activated by a nonlinear function, and fμd⋅ and fσd⋅ are linear neural perceptual functions.Based on this, the following loss function can be directly minimized for each set of instancesXi,Si during the training process:(14)ℒCANTM=−∑k=1Mdlogθd⋅β∗,xkd+DKLqθdpθd.Therefore, all parameters in CANTM can be inferred, and the potential competency themes involved in each position can be further explored.


## 5.3. CANJRG

After learning about the potential competency themes through CANTM, this subsection describes how to use the encoder-decoder neural model to generate job requirements. As shown in Figure3, it contains two main components, including a sequence encoder that extracts semantic information from the input job responsibilities Xi, and a sequence decoder under competency awareness, which can generate each word in the job requirements Yi by the guidance of potential competency themes.Figure 3
Schematic diagrams of the attention mechanism under ability perception: (a) calculation of attention mechanism of ability perceptionμjt and (b) calculation of attention mechanism of ability perception μj,kc.Sequence encoder: first use an embedding layer to find the embedding vectorekd for each word xk in Xi and then use a Bi-LSTM [5, 33] to encode the sequence e1d,e2d,…,eMdd: hkd⟶=LSTMekd,hk−1d⟶,hkd¯=LSTMekd,hk+1d←, where ekd=Wedxk is the word vector of xk and the LSTM is a unidirectional LSTM network. Finally, hkd=hkd⟶;hkd← is used to represent the final hidden vector of the xk sequence encoder.Competency-aware sequence decoder: the following describes how to construct a decoder to generate each word in job requirementYi. In the generation process, the competency topic tj is first estimated for each sentence yj in Yi, and then each word is predicted the following probability yj,k:(15)pyj,k|Xi=pyj,k|y<j,yj,<k,ℋ,θs,tj,where y<j represents the sequence y1,y2,…,yj−1,yj,<k and represents yj,1,yj,2,…,yj,k−1andℋ=h1d,h2d,…,hMdd represents the implicit state of all sequence encoders. θs is Yi; the implicit capability topic vector learned through CANTM, and tj∈1,Ks is the topic label for each utterance yj.Specifically, the sequence decoder under capability awareness is constructed based on two one-way LSTMs [34]. hjt and hj,kc represent the implicit status of the competent topics tj and words yj,k computed by the LSTM, respectively.(16)hjt=LSTMej−1t;θs;hj−1,Mj−1cc,hj−1thj,kc=LSTMej,k−1c;θs;hjt,hj,k−1c,ejt=Wee′tj,ej,kc=Wecyj,k,where ejt and ej,kc is the embedded representations of tj and yj,k. Mjc is the length of statement yj. Also, implicit states h0t and h1,0c are initialized by hMdd, and hj,0c is initialized by hj−1,Mj−1cc. In addition, two capability-aware attention mechanisms are designed here to capture contextual features from H to enhance the performance of the generation process as follows:(17)αl,jt=expgl,jt∑p=1Nexpgp,jt,αl,j,kc=expgl,j,kc∑p=1Mjcexpgp,j,kc,gl,jt=vα1⊤tanhWα1hld;hjt;θs+bα′,gl,j,kc=vαc⊤tanhWαchld;hj,kc;θs+bαc.The capability-aware context vectorsujt and uj,kc can then be calculated by the following equations:(18)ujt=∑l=1Nαl,jthld,uj,kc=∑l=1Mjcαl,j,kchld.The ability theme labels can then be predicted.tj and yj for each word in yj,k as follows:(19)ptj|t<j,ℋ,θs=softmaxWthjt;ujt;θs+bt,pyj,k|y<j,yj,<k,ℋ,θs,tj=softmaxWchj,kc;uj,kc;θs+bc.In addition, an ability-aware replication mechanism is designed such that the proposed decoder can directly replicate the words in the ability vocabulary. Specifically, a generation probability is defined here whenyj generates the k-th word: yj,k; pgen∈0,1.The probability distribution of the predicted words based on the ability word list can then be updated with the following equation:(20)pyj,k|y<j,yj,<k,ℋ,θs,tj=pgensoftmaxWchj,kc;uj,kc;θs+bc+1−pgenβstj,where βstj is the word distribution in topic.Finally, in the heterogeneous model, for each group of training instancesXi,Yi, the parameters in the model are learned by minimizing the following cross-entropy loss function:(21)ℒCTL=−∑j=1Nlogptj|Xi,θs,ℒGJR=−∑j=1N∑k=1Mjslogpyj,k|Xi,θs.


## 5.4. Capability-Aware Policy Gradient Training Algorithm (CAPGTA)

Before introducing CAPGTA, a basic end-to-end training approach will be shown to learn all the parameters in the above two models. Specifically, because of the CANTM neural variation, the loss functionsℒCANTM, ℒCTL and ℒGJR, can be trained jointly at the same time.ℒ∗=ℒGJR+λ1ℒCANTM+λ2ℒCTL, where λ1 and λ2 are hypermastigote to balance each model. The teacher forcing algorithm is used in the training process, that is, the previous real word yj,k−1 is used in the training to calculate hjt and hj,kc. For the ability topic tj−1, the following is used for generation:(22)tj−1=argmaxlθsl∏k=1Mj−1sβ∗,sj−1,ksll=1Ks.And the predicted values are used as input in the testing sessions.Direct minimizationℒ∗ does not always generate the best job requirements because it does not directly optimize discrete assessment metrics such as ROUGE and BLKJ [35]. In addition, it is desired here that the accuracy of the competencies involved in the generated job requirements can be optimized more intuitively, so that the rationality and validity of the generated results can be better ensured.Some recent reinforcement learning techniques can be used to solve this nondifferentiable task metric problem. Here, the combination of CANTM and CANJRG can be considered as an agent [30, 36], which interacts with the environment, that is, the training instances. Given an input job duty X, strategy pol−icypθyj,kX,y^<j,yj<k is determined by the parameter 0 of the intelligence for each action, that is, predicting the next word based on the current state. Until the end position (EOS) of the sequence of job requirements is generated, a reward will be observed. The goal of the whole training is to learn the strategy by minimizing the negative expected reward of(23)ℒRL=−EY^∼pθY^|XrY^.Based on reinforcement learning algorithm, it obtains that(24)∇θℒRL=−EY^∼pθY^|XrY^∇θlogpθY^|X.It is possible to use a simple Monte–Carlo samplingY^ based strategy pθ as follows:(25)∇θℒRL≈−rY^∇θlogpθY^|X=−rY^∇θ∑j=1N∑k=1Mjclogpθyj,kX,y^<j,yj<k=−rY^∇θ∑j=1Nlogpθt^j|X,t^<j+∑k=1Mjclogpθyj,kX,y^<j,yj<k,where t^1,…,t^N is a Monte–Carlo [37] sample of the capability label. pθt^j|X,t^<j and pθyj,kX,y^<j,yj<k are calculated from equations (23) and (24), respectively.(26)∇θℒRL≈rY^g−rY^∇θ∑j=1Nlogpθt^j|X,t^<j+∑k=1Mjclogpθyj,kX,y^<j,yj<k.As mentioned earlier, here, it is desired to directly optimize the accuracy of the competencies in the generated job requirements. Therefore, we use theF1 values [38] of the generated skill terms as a reward function, that is,(27)rcapabilityY^=2CardS^∩SCardS^+CardS,where S is the set of skill words in the actual job requirements and S^ is the set of skill words in Y^, representing the set size. The ROUGE-1 score is also incorporated into the reward function, which is used to measure statistical information based on the longest common subsequence between the actual and model-generated job requirements. This allows a direct optimization of the similarity of the sentence hierarchy to authenticity, which helps improve the fluency of the generated text. The reward function can then be set to(28)rY^=λ3rcapabilityY^+Rouge−1Y^,Y.Finally,ℒ∗ and ℒRL are used jointly to obtain the overall learning objective function as follows:(29)ℒ=1−γℒ∗+γℒRL,where γ is the dynamic hypernatremia during the training process. It is first set to 0 for a period of training alone ℒ∗, and then the value is gradually increased.


## 6. Experimental Analysis

This section presents the results of extensive experiments of quantitative analysis and manual evaluation on real-world HR data sets [4, 12] to demonstrate the effectiveness of the proposed Cajon in job skill prediction and job requirement generation.


### 6.1. Experimental Data

Two real-world HR data sets [4, 12], including technical (T) and product P related job data sets, are used here. Specifically, 3,475 and 2,351 different jobs were collected, respectively, including their job responsibilities and corresponding job requirement texts, which have been carefully proofread by six HR experts to ensure fluency and reasonableness. Some statistics are shown in Table 1 and Figures 4 and 5. In the experiments, 80% of the data set were randomly selected as training data, another 10% as test data to verify the performance, and the last 10% was used to tune the parameters.Table 1
Statistical description ofT and P data sets.
Data setStatistical indicatorsValueTTotal number3,475Average number of words for job duties50.11Average word count of job requirements100.10Average number of words of ability for job requirements25.72Average number of sentences for job requirements6.95PTotal number2,351Average number of words for job duties74.98Average word count of job requirements75.80Average number of words of ability for job requirements15.58Average number of sentences for job requirements4.98Figure 4
Text statistics of the technology (T) data set.Figure 5
Text statistics of the productP data set.In addition to generating reasonable skill words in job requirements, LSTM-CRF [15, 25] model was trained to extract possible competency words based on the method of the paper. With the help of HR experts, a final vocabulary containing 4,825 skill entities was obtained.


### 6.2. Training Parameters and Environment Setting

In the competency-aware neural topic model, the raw input of job responsibilities and competency words from the job descriptions is first converted into a bag-of-words vector [4, 23]. And before that, deactivated words and high- and low-frequency words are removed to enhance the model. The performance of the model is enhanced by removing the discontinued words and high- and low-frequency words. Here, the number of topics Kd,Ks is set to (30, 50) and (30, 30) for the T and P data sets, respectively. In addition, we add batch normalization in computing μdXi,Si,μsXi,Si,logσdXi,Si, and logσsXi,Si to avoid the problem that KL divergence disappears during training.In the capability-aware postrequirement generation model, the embedding layer sizes of wordxk,yj,k, and topic tag tk are 128, 128, and 50, respectively. Sequence encoder is implemented by a bidirectional LSTM, and the hidden layer size of each LSTM layer is 256. Capability-aware sequence decoder is implemented by two unidirectional LSTMs, both of which have a hidden layer size of 256. In addition, the size of the hidden layer states in both the capability-aware attention mechanism and the capability-aware replication mechanism are also set to 256.During the training of the complete Cajon framework, the parameters are initialized using the Xavier strategy.Then 200 rounds of pretraining are performed on CANTM. After that, we setλ1=1 and λ2=1 to train the part of Cajon other than the reinforcement learning loss function. Finally, we set λ3=1 and incrementally increase to train our model by equation (12). In addition, Adam is used as the optimizer, and the initial learning rate is set to 0.001. And, the gradient crop is also set to 1.0 to stabilize the training process. In the test phase of generation, we used the Beam Search algorithm and set the cluster size to 4.The overall experiments were performed on a Linux server configured with RedHat 4.8.536, 2.40 G Hz Intel(R) Xeon(R) Gold6148 CPU; models were developed based on the tensor flow framework.


### 6.3. Benchmarking Algorithm

To evaluate the effectiveness of the proposed approach, several state-of-the-art text generation methods are compared here, and these methods are adapted to fit the problem definition setting.Seq2Seq [14] is a classical text-to-text generation model, which was proposed in the paper with the aim of implementing neural machine translation. In the experiments of this section, a concat-based approach to compute attention mechanisms is also applied, which is similar to the approach proposed in this chapter.Kit [18] is a variant of Seq2Seq, a model that implements a pointer network and an overlay mechanism to handle the automatic digest problem.Kid is a natural language generation model based on transformer networks, which are proposed to solve the sequence-to-sequence generation problem.In addition, state-of-the-art automated job description writing methods are compared.SAMA [19, 21] is the state-of-the-art automated job description writing model, which is presented in the paper. For a fair comparison with the proposed model, the characteristics of the additional information it uses (e.g., company size) are removed in this section of the experiments.In addition, four variants of the Cajon framework are compared to assess the impact of each component of the model on the generated results: Cajon (w/o RL) is a variant of Cajon in which the CAPGTA is removed from the training, that is, the training is done directly by the formula. Cajon (w/o RL,ℒCANTM) is a variant of Cajon (w/o RL, ℒCTL) that removes the ability topic label related part of the sequence decoder, that is, only θs is used to introduce ability topic information.Cajon (w/o RL, topic-copy) is a variant of Cajon (w/o RL), which removes the capability-aware replication-based mechanism.


### 6.4. Evaluation Indicators

In order to evaluate the effectiveness of job requirement generation, both automatic and manual assessments were used.In the automatic evaluation, standard ROUGE metrics were used, including ROUGE-1, ROUGE-2, and ROUGE-L, which measure the statistics of unary word overlap, binary word overlap, and longest common subsequence (LCS) [31] in the comparison of real and automatically generated results, respectively. The BLEU evaluation metric, which measures the cooccurrence of n-words, was also used. Finally, the precision rate, recall rate, and F1 value of skill words in job requirements are used to automatically verify the rationality and validity of the generated results, as shown in Table 2.Table 2
Experimental results of Cajon and comparison methods.
Data setMethodROUGEBLEUManual evaluation metricsROUGE-1ROUGE-2ROUGE-LBLEU-1 (%)BLEU-4 (%)FluencyValidityTSeq2Seq0.63890.41510.599637.3216.223.663.58pGNet0.64130.42460.606138.1416.753.703.61UniLM0.61670.39650.586334.6412.293.523.14SAMA0.60040.32770.545539.3015.073.473.52Cajon0.65310.42960.607741.8119.413.733.81PSeq2Seq0.72360.50850.713832.027.283.633.34pGNet0.74210.54100.733132.728.283.673.41UniLM0.75420.54390.745231.436.423.463.28SAMA0.71600.47880.707434.769.003.553.49Cajon0.75220.57570.753136.4610.843.783.74Figure6 shows the accuracy, recall, and F1 value data set of Cajon and its variants; the proposed model improves 1.06% and 4.60% in automatic metrics ROUGE-1 and BLEU-1, and 3.00% and 7.16% in manual metrics Fluency and Validity, respectively, compared to the best available techniques. This result clearly demonstrates the effectiveness of the proposed model in generating fluent and reasonable job requirements [39].Figure 6
Different model performance: (a) technology (T) data set and (b) product P data set.
(a)(b)In addition, Figure6 shows the precision, recall, and F1 values of the generated competency words in the job requirements. Here, it can be found that the proposed model outperforms the best results of all benchmarks of 9.49%, 3.55%, and 6.73% in the technical data and 20.62%, 5.29%, and 17.69% in the product data set, respectively. It clearly validates that the generated results of the proposed framework can more accurately capture the relevant and representative skill requirements of the position.Ablation experiments: here, the effects of the proposed model and its variants are compared. And Seq2Seq can also be used as a variant of the proposed method, that is, the CANTM model is removed. Obviously, it is clear from the results that all model components can enhance the effect. Specifically, the performance decreases rapidly when only potential capability topic information is considered, which proves the importance of predicting potential capability topic labels for the decoder. As shown in Figure7, the capability-awareness-based attention mechanism can improve about 2.61% and 1.38% of ROUGE-1 and BLEU-I, respectively, in the technology data set, and 2.53% and 4.83% in the product data set, respectively. Meanwhile, the capability-awareness-based replication mechanism can improve 1.87% and 0.84% in the technical data set on ROUGE-1 and BLEU-1 white spoon effect and 2.92% and 1.54% in the product data set, respectively. In addition, Figure 8 shows that the proposed CAPGTA can effectively improve the precision, recall, and F1 values of skill words in generating job requirements.Figure 7
Effect of technical data setsKd and Ks on model effects: (a) ROUGE-1, (b) ROUGE-L, and (c) BLEU-1.
(a)(b)(c)Figure 8
Effect of product data setsKd and Ks on model effects: (a) ROUGE-1, (b) ROUGE-L, and (c) BLEU-1.
(a)(b)(c)Subject number parameter experiments: as shown in Figure8, to evaluate the parameter sensitivity, Cajon is trained here by tuning parameters Kd and Ks, 0 to 100, whose other ones are fixed at Kd=30 and Ks=50 in the technical data set and Kd=30 and Ks=30 in the product data set. Here, it can be clearly observed that the best results can be obtained in the technical data set Kd=30 and Ks=30 and in the product data set Ks=30 and Kd=30.


### 6.5. Generating Example Studies and Discussion

To further illustrate the effectiveness and interpretability of the proposed framework, an example of job requirements generated by Cajon is given in Figure9. Given the position to hire a data mining algorithm engineer, it can be found that the generated results are fluent and include competency requirements regarding education, work experience, data mining algorithms, basic programming language, and teamwork, most of which are mentioned in the job requirements written by experts. This proves that the proposed model is effective in generating fluent and reasonable job requirements. In addition when generating each job requirement statement, a word cloud representation corresponding to the predicted competency topic is shown. For this reason, it can be seen that the proposed CANTM can effectively learn meaningful competency themes, demonstrating that potential competency themes can effectively guide the generation of job requirement texts, thus demonstrating the interpretability of the proposed framework.Figure 9
Case study diagrams generated from job requirements, with skill words highlighted.


## 6.1. Experimental Data

Two real-world HR data sets [4, 12], including technical (T) and product P related job data sets, are used here. Specifically, 3,475 and 2,351 different jobs were collected, respectively, including their job responsibilities and corresponding job requirement texts, which have been carefully proofread by six HR experts to ensure fluency and reasonableness. Some statistics are shown in Table 1 and Figures 4 and 5. In the experiments, 80% of the data set were randomly selected as training data, another 10% as test data to verify the performance, and the last 10% was used to tune the parameters.Table 1
Statistical description ofT and P data sets.
Data setStatistical indicatorsValueTTotal number3,475Average number of words for job duties50.11Average word count of job requirements100.10Average number of words of ability for job requirements25.72Average number of sentences for job requirements6.95PTotal number2,351Average number of words for job duties74.98Average word count of job requirements75.80Average number of words of ability for job requirements15.58Average number of sentences for job requirements4.98Figure 4
Text statistics of the technology (T) data set.Figure 5
Text statistics of the productP data set.In addition to generating reasonable skill words in job requirements, LSTM-CRF [15, 25] model was trained to extract possible competency words based on the method of the paper. With the help of HR experts, a final vocabulary containing 4,825 skill entities was obtained.


## 6.2. Training Parameters and Environment Setting

In the competency-aware neural topic model, the raw input of job responsibilities and competency words from the job descriptions is first converted into a bag-of-words vector [4, 23]. And before that, deactivated words and high- and low-frequency words are removed to enhance the model. The performance of the model is enhanced by removing the discontinued words and high- and low-frequency words. Here, the number of topics Kd,Ks is set to (30, 50) and (30, 30) for the T and P data sets, respectively. In addition, we add batch normalization in computing μdXi,Si,μsXi,Si,logσdXi,Si, and logσsXi,Si to avoid the problem that KL divergence disappears during training.In the capability-aware postrequirement generation model, the embedding layer sizes of wordxk,yj,k, and topic tag tk are 128, 128, and 50, respectively. Sequence encoder is implemented by a bidirectional LSTM, and the hidden layer size of each LSTM layer is 256. Capability-aware sequence decoder is implemented by two unidirectional LSTMs, both of which have a hidden layer size of 256. In addition, the size of the hidden layer states in both the capability-aware attention mechanism and the capability-aware replication mechanism are also set to 256.During the training of the complete Cajon framework, the parameters are initialized using the Xavier strategy.Then 200 rounds of pretraining are performed on CANTM. After that, we setλ1=1 and λ2=1 to train the part of Cajon other than the reinforcement learning loss function. Finally, we set λ3=1 and incrementally increase to train our model by equation (12). In addition, Adam is used as the optimizer, and the initial learning rate is set to 0.001. And, the gradient crop is also set to 1.0 to stabilize the training process. In the test phase of generation, we used the Beam Search algorithm and set the cluster size to 4.The overall experiments were performed on a Linux server configured with RedHat 4.8.536, 2.40 G Hz Intel(R) Xeon(R) Gold6148 CPU; models were developed based on the tensor flow framework.


## 6.3. Benchmarking Algorithm

To evaluate the effectiveness of the proposed approach, several state-of-the-art text generation methods are compared here, and these methods are adapted to fit the problem definition setting.Seq2Seq [14] is a classical text-to-text generation model, which was proposed in the paper with the aim of implementing neural machine translation. In the experiments of this section, a concat-based approach to compute attention mechanisms is also applied, which is similar to the approach proposed in this chapter.Kit [18] is a variant of Seq2Seq, a model that implements a pointer network and an overlay mechanism to handle the automatic digest problem.Kid is a natural language generation model based on transformer networks, which are proposed to solve the sequence-to-sequence generation problem.In addition, state-of-the-art automated job description writing methods are compared.SAMA [19, 21] is the state-of-the-art automated job description writing model, which is presented in the paper. For a fair comparison with the proposed model, the characteristics of the additional information it uses (e.g., company size) are removed in this section of the experiments.In addition, four variants of the Cajon framework are compared to assess the impact of each component of the model on the generated results: Cajon (w/o RL) is a variant of Cajon in which the CAPGTA is removed from the training, that is, the training is done directly by the formula. Cajon (w/o RL,ℒCANTM) is a variant of Cajon (w/o RL, ℒCTL) that removes the ability topic label related part of the sequence decoder, that is, only θs is used to introduce ability topic information.Cajon (w/o RL, topic-copy) is a variant of Cajon (w/o RL), which removes the capability-aware replication-based mechanism.


## 6.4. Evaluation Indicators

In order to evaluate the effectiveness of job requirement generation, both automatic and manual assessments were used.In the automatic evaluation, standard ROUGE metrics were used, including ROUGE-1, ROUGE-2, and ROUGE-L, which measure the statistics of unary word overlap, binary word overlap, and longest common subsequence (LCS) [31] in the comparison of real and automatically generated results, respectively. The BLEU evaluation metric, which measures the cooccurrence of n-words, was also used. Finally, the precision rate, recall rate, and F1 value of skill words in job requirements are used to automatically verify the rationality and validity of the generated results, as shown in Table 2.Table 2
Experimental results of Cajon and comparison methods.
Data setMethodROUGEBLEUManual evaluation metricsROUGE-1ROUGE-2ROUGE-LBLEU-1 (%)BLEU-4 (%)FluencyValidityTSeq2Seq0.63890.41510.599637.3216.223.663.58pGNet0.64130.42460.606138.1416.753.703.61UniLM0.61670.39650.586334.6412.293.523.14SAMA0.60040.32770.545539.3015.073.473.52Cajon0.65310.42960.607741.8119.413.733.81PSeq2Seq0.72360.50850.713832.027.283.633.34pGNet0.74210.54100.733132.728.283.673.41UniLM0.75420.54390.745231.436.423.463.28SAMA0.71600.47880.707434.769.003.553.49Cajon0.75220.57570.753136.4610.843.783.74Figure6 shows the accuracy, recall, and F1 value data set of Cajon and its variants; the proposed model improves 1.06% and 4.60% in automatic metrics ROUGE-1 and BLEU-1, and 3.00% and 7.16% in manual metrics Fluency and Validity, respectively, compared to the best available techniques. This result clearly demonstrates the effectiveness of the proposed model in generating fluent and reasonable job requirements [39].Figure 6
Different model performance: (a) technology (T) data set and (b) product P data set.
(a)(b)In addition, Figure6 shows the precision, recall, and F1 values of the generated competency words in the job requirements. Here, it can be found that the proposed model outperforms the best results of all benchmarks of 9.49%, 3.55%, and 6.73% in the technical data and 20.62%, 5.29%, and 17.69% in the product data set, respectively. It clearly validates that the generated results of the proposed framework can more accurately capture the relevant and representative skill requirements of the position.Ablation experiments: here, the effects of the proposed model and its variants are compared. And Seq2Seq can also be used as a variant of the proposed method, that is, the CANTM model is removed. Obviously, it is clear from the results that all model components can enhance the effect. Specifically, the performance decreases rapidly when only potential capability topic information is considered, which proves the importance of predicting potential capability topic labels for the decoder. As shown in Figure7, the capability-awareness-based attention mechanism can improve about 2.61% and 1.38% of ROUGE-1 and BLEU-I, respectively, in the technology data set, and 2.53% and 4.83% in the product data set, respectively. Meanwhile, the capability-awareness-based replication mechanism can improve 1.87% and 0.84% in the technical data set on ROUGE-1 and BLEU-1 white spoon effect and 2.92% and 1.54% in the product data set, respectively. In addition, Figure 8 shows that the proposed CAPGTA can effectively improve the precision, recall, and F1 values of skill words in generating job requirements.Figure 7
Effect of technical data setsKd and Ks on model effects: (a) ROUGE-1, (b) ROUGE-L, and (c) BLEU-1.
(a)(b)(c)Figure 8
Effect of product data setsKd and Ks on model effects: (a) ROUGE-1, (b) ROUGE-L, and (c) BLEU-1.
(a)(b)(c)Subject number parameter experiments: as shown in Figure8, to evaluate the parameter sensitivity, Cajon is trained here by tuning parameters Kd and Ks, 0 to 100, whose other ones are fixed at Kd=30 and Ks=50 in the technical data set and Kd=30 and Ks=30 in the product data set. Here, it can be clearly observed that the best results can be obtained in the technical data set Kd=30 and Ks=30 and in the product data set Ks=30 and Kd=30.


## 6.5. Generating Example Studies and Discussion

To further illustrate the effectiveness and interpretability of the proposed framework, an example of job requirements generated by Cajon is given in Figure9. Given the position to hire a data mining algorithm engineer, it can be found that the generated results are fluent and include competency requirements regarding education, work experience, data mining algorithms, basic programming language, and teamwork, most of which are mentioned in the job requirements written by experts. This proves that the proposed model is effective in generating fluent and reasonable job requirements. In addition when generating each job requirement statement, a word cloud representation corresponding to the predicted competency topic is shown. For this reason, it can be seen that the proposed CANTM can effectively learn meaningful competency themes, demonstrating that potential competency themes can effectively guide the generation of job requirement texts, thus demonstrating the interpretability of the proposed framework.Figure 9
Case study diagrams generated from job requirements, with skill words highlighted.


## 7. Conclusions

In this paper, an end-to-end competency-aware neural job requirement generation framework is proposed to automate the generation of job requirements, and the prediction of skill words in job requirements can be achieved based on the prediction of competency themes. Then an encoder-decoder recurrent neural network is proposed to implement a job requirement generation, followed by a competency-aware policy gradient-based training algorithm to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants in comparison with state-of-the-art benchmarks.



---

*Source: 1657236-2021-10-23.xml*