SlowGuess/ACL_2020_OCR huggingface dataset:
In addition to the overall metrics, Figure 4 further demonstrates the skill-level metrics. Figure 4 demonstrates that the job requirements generated by skill aware models (SAMA(w/o pred), SAMA(w/o graph), and SAMA) consist of more accurate and richer skills than those generated by the baselines (S2SA, DelNet, and VPN). Among them, SAMA achieves the best performance. Besides, SAMA(w/o graph) obtains a higher recall rate, which demonstrates that it can enrich the skill information effectively. SAMA(w/o pred) obtains a higher precision rate, which demonstrates that it can refine the skill information effectively.
4.2.2 Human-based Evaluation
Results of the human-based annotation are shown in Table 3. it can be seen that skill aware models obtain more relevant and informative results (good results) than the baselines, and SAMA obtains the most "good" results and the least "bad" results. The results are consistent with the automatic metric results. S2SA obtains the most "normal" results. This is because S2SA contains less rich and accurate skills in job requirements although with a good fluency. DelNet and VPN obtain a large percentage
Model	bad	normal	good	Kappa
S2SA	0.34	0.50	0.16	0.44
DelNet	0.48	0.34	0.18	0.41
VPN	0.56	0.32	0.12	0.38
SAMA(w/o pred)	0.28	0.42	0.30	0.42
SAMA(w/o graph)	0.26	0.42	0.32	0.43
SAMA	0.22	0.40	0.38	0.42
Table 3: Human-based evaluation results
of "bad" results mainly because of the repeated sentences. Besides, SAMA(w/o pred) and SAMA(w/o graph) are both much worse than SAMA on "good" results. This is because SAMA(w/o pred) misses some skills, and SAMA(w/o graph) misuses some skills. All models have the kappa scores around 0.4, indicating that evaluators reach high agreement.
4.2.3 Visualization Analysis
When the model generates the target sequence, there exist differences in the contributions of different words. SAMA can synthetically select the most informative words by utilizing the three attention mechanisms. Figure  $5^{6}$  shows the visualization of three attention mechanisms. According to Figure 5, when SAMA generates the skill "EA (Environmental Art)", it automatically assigns larger weights to more informative words in three sources, e.g., 'interior' of  $X$ , 'interior, design, construction, matching' of  $O$ , 'interior, design, drawing, management' of  $S$ . It shows that SAMA can consider the different contributions and capture the most informative words automatically from multiple sources.
4.2.4 Case Study
To illustrate the difference in quality between SAMA and the compared models, we give an example of the generated text in Figure 6, where we
Input:  
1、负责完成公司下达的年度销售指标。2、将年度指标分解至季度、月度并加以执行。3、确保客户订单及时回款，确保无逾期、呆账等。4、渠道新客户开发及老客户的维护。  
1. Responsible for completing the annual sales targets issued by the company. 2. Decompose annual indicators into quarters and months then implement them. 3. Ensure that orders are repaid timely and ensure no overdue or bad debts. 4. Develop new customer and maintain old customers.
Gold Output: 1、高中以上学历，具备一定的销售经验。2、有礼赠品团购渠道销售经验者优先。3、忠诚度高、服从管理、有团队协作精神。 1. High school education above, with some sales experience. 2. Sales experience in gift group-buying is preferred. 3. High loyalty, obedient management, and teamwork spirit.
SAMA Output:  
1、高中以上学历，1年以上销售经验，有销售运营类管理更加的优先考虑；2、有礼赠品团购终端客户服务体系的工作经验、熟悉礼品销售者优先；3、有团队合作精神，能承受较大的工作压力。  
1. High school education above, more than 1 year of sales experience, sales management is preferred; 2. Working experience in gift group-buying terminal customer service system, familiar with gift sales are preferred; 3. Team spirit, can bear high working pressure.
S2SA Output:  
1、高中及以上学历，市场营销等相关专业；2、2年以上销售行业工作经验，有朝艺门窗或建材行业销售经验者优先。
1. High school education or above, marketing and other related majors; 2. More than 2 years working experience in sales, sales experience in aluminum doors and windows or building materials industry is preferred.
Figure 6: Case Study. We translate Chinese to English. Skills in bold print are the correct and accurate skills. The underlined skills are the correct but inaccurate skills. The italic skills are the incorrect skills.
compare SAMA with the strong baseline S2SA. As shown in Figure 6, SAMA captures all three aspects the same as ground truth, while S2SA misses the third aspect. Besides, in every aspect SAMA generates more correct and accurate skills, while S2SA obviously performs not good enough and generates inaccurate skills. Generally, the main consideration of job seekers is the skills they need to master, such as Python, English, and Go Language. Therefore, although S2SA generates some right words, like "preferred", it does not increase the quality of the generated text because it generates inaccurate skills.
4.2.5 Parameter Analysis
We show how the two key hyperparameters of SAMA,  $\lambda$  and  $\mu$ , influence the performance in Figure 7. The hyperparameter  $\lambda$  adjusts the balance of the probabilities between  $P_{local}$  and  $P_{global}$  and  $\mu$  adjusts the balance between two losses, the loss in skill prediction  $L^{S}$  and the loss in job requirements generation  $L^{Y}$ .
The value of hyperparameter  $\lambda$  varies from 0.1 to 0.9 and bigger value implies more global prior knowledge of skills. Figure 7 shows that the performance gets a peak when the  $\lambda$  increases. It is intuitive that prior knowledge can help generate
Due to the space limitation, we intercept some texts.
Figure 7: Parameter analysis.
accurate and rich skills. However, the too large value may sacrifice the fluency.
The value of hyperparameter  $\mu$  varies from 1.1 to 2.0. We give greater weight to the loss of job requirements generation for the reason that it is the target of the JPG task. As observed in Figure 7, a weight close to 1 may introduce noises from the skill labels. Besides, when the weight continuously increases close to 2, the model is incapable of fully considering the skill labels.
The related works fall into two categories, human resource management and generation models.
5.1 Human Resource Management
Human Resource Management (HRM) is an appealing topic for applied researchers, and the recruitment is a key part of HRM. With the explosive growth of recruiting data, many studies focus on the efficient automatic HRM, e.g., person-organization fit, intelligent job interview, and job skill ranking. Lee and Brusilovsky (2007) designed a job recommender system with considering the preferences of both employers and candidates. Qin et al. (2019) proposed a personalized question recommender system for job interview to better interview the candidates. Naim et al. (2015) analyzed the videos of interview for quantifying verbal and nonverbal behaviors in the context of job interviews. Sun et al. (2019) studied the compatibility of person and organization. Xu et al. (2018) proposed a data driven approach for modeling the popularity of job skills. Besides, some augmented writing tools, such as Textio  $^{7}$  and TapRecruit  $^{8}$ , are developed to assist the HR to write job postings in the way that assuming a draft as input and then polishing the draft.
In this paper, we also consider improving the efficiency of HRM from the perspective of the job posting writing which is the crucial first step in the process of recruitment.
5.2 Generation Models
Many practical applications are modeled as generation tasks such as keyword extraction, headline generation, and response generation. Many generation tasks are formulated as Seq2Seq learning problems. Plenty of studies focused on the optimization of the Seq2seq model. For example, Lopyrev (2015) trained a Seq2Seq model with attention for headlines generation task. Xing et al. (2017) incorporated topic information into Seq2Seq by a joint attention mechanism to generate informative responses for chatbots. Meng et al. (2017) applied a Seq2seq model with a copy mechanism to a keyword extraction task.
However, models without explicit modeling the sentence planning have a great limitation in generating complex argument structures depending on hierarchy. Dong and Lapata (2018) decomposed the semantic parsing process into sketch generation and details filled-in and proposed a structure-aware neural architecture. Zhang et al. (2019) formulated outline generation task as a hierarchical structured prediction problem and proposed HiStGen. Puduppully et al. (2019) proposed a two-stage model which incorporates content selection and planning, for the data-to-text generation task.
Similar to the above researches, we proposed a hierarchical generation model, namely SAMA, which first labels the job description with multiple skills and then generates the job requirement paragraph, to tackle the JPG task. Different from prior arts, SAMA considered the global information across the whole dataset to generate high quality job requirements.
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.
<sup>7</sup>https://textio.com/products/
<sup>8</sup>https://taprecruit.co/
This research was supported by the National Natural Science Foundation of China under grant No. 61976119, the Natural Science Foundation of Tianjin under grant No. 18JCYBJC15800, and the Major Program of Science and Technology of Tianjin under grant No. 18ZXZNGX00310.
Li Dong and Mirella Lapata. 2018. Coarse-to-fine decoding for neural semantic parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 731-742.
Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging.
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR.
Danielle H Lee and Peter Brusilovsky. 2007. Fighting information overflow with personalized comprehensive information access: A proactive job recommender. In ICAS'07, pages 21-21.
Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries.
Cao Liu, Shizhu He, Kang Liu, and Jun Zhao. 2019. Vocabulary pyramid network: Multi-pass encoding and decoding with multi-level vocabularies for response generation. In ACL, pages 3774-3783.
Konstantin Lopyrev. 2015. Generating news headlines with recurrent neural networks.
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In EMNLP, pages 1412-1421.
Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, and Yu Chi. 2017. Deep keyphrase generation. In ACL, pages 582-592.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations.
Iftekhar Naim, M Iftekhar Tanveer, Daniel Gildea, and Mohammed Ehsan Hoque. 2015. Automated prediction and analysis of job interview performance: The role of what you say and how you say it. In International Conference and Workshops on Automatic Face and Gesture Recognition, pages 1-6.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318.
Ratish Puduppully, Li Dong, and Mirella Lapata. 2019. Data-to-text generation with content selection and planning. In The Thirty-Third AAAI Conference on Artificial Intelligence, pages 6908-6915.
Chuan Qin, Hengshu Zhu, Chen Zhu, Tong Xu, Fuzhen Zhuang, Chao Ma, Jingshuai Zhang, and Hui Xiong. 2019. Duerquiz: A personalized question recommender system for intelligent job interview. In KDD, pages 2165-2173.
Mike Schuster and Kuldip K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Trans. Signal Processing, 45(11):2673-2681.
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liquin Chen, Xin Wang, and Lawrence Carin. 2019. Hierarchically-structured variational autoencoders for long text generation.
Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, and Hui Xiong. 2019. The impact of person-organization fit on talent management: A structure-aware convolutional neural network approach. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1625–1633.
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, and Tie-Yan Liu. 2017. Deliberation networks: Sequence generation beyond one-pass decoding. In Advances in Neural Information Processing Systems, pages 1784-1794.
Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017. Topic aware neural response generation. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pages 3351-3357.
Tong Xu, Hengshu Zhu, Chen Zhu, Pan Li, and Hui Xiong. 2018. Measuring the popularity of job skills in recruitment market: A multi-criteria approach. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 2572-2579.
Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang. 2018. SGM: sequence generation model for multi-label classification. In Proceedings of the 27th International Conference on Computational Linguistics.
Xingdi Yuan, Tong Wang, Rui Meng, Khushboo Thaker, Daqing He, and Adam Trischler. 2018. Generating diverse numbers of diverse keyphrases.
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization.
Min-Ling Zhang and Zhi-Hua Zhou. 2014. A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng., 26(8):1819-1837.
Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, and Xueqi Cheng. 2019. Outline generation: Understanding the inherent content structure of documents. In SIGIR, pages 745-754.