Understanding the evolution of job requirements is becoming more important
for workers, companies and public organizations to follow the fast
transformation of the employment market. Fortunately, recent natural language
processing (NLP) approaches allow for the development of methods to
automatically extract information from job ads and recognize skills more
precisely. However, these efficient approaches need a large amount of annotated
data from the studied domain which is difficult to access, mainly due to
intellectual property. This article proposes a new public dataset, FIJO,
containing insurance job offers, including many soft skill annotations. To
understand the potential of this dataset, we detail some characteristics and
some limitations. Then, we present the results of skill detection algorithms
using a named entity recognition approach and show that transformers-based
models have good token-wise performances on this dataset. Lastly, we analyze
some errors made by our best model to emphasize the difficulties that may arise
when applying NLP approaches.

本文提出了一个包含许多软技能注释的保险工作提供的新的公共数据集 FIJO，介绍了其特点和局限性，然后使用命名实体识别方法展示了技能检测算法的结果，最后分析了应用自然语言处理方法时可能出现的困难。

FIJO：法国保险软技能检测数据集

"FIJO": a French Insurance Soft Skill Detection Dataset

The wide spread use of online recruitment services has led to information
explosion in the job market. As a result, the recruiters have to seek the
intelligent ways for Person Job Fit, which is the bridge for adapting the right
job seekers to the right positions. Existing studies on Person Job Fit have a
focus on measuring the matching degree between the talent qualification and the
job requirements mainly based on the manual inspection of human resource
experts despite of the subjective, incomplete, and inefficient nature of the
human judgement. To this end, in this paper, we propose a novel end to end
Ability aware Person Job Fit Neural Network model, which has a goal of reducing
the dependence on manual labour and can provide better interpretation about the
fitting results. The key idea is to exploit the rich information available at
abundant historical job application data. Specifically, we propose a word level
semantic representation for both job requirements and job seekers' experiences
based on Recurrent Neural Network. Along this line, four hierarchical ability
aware attention strategies are designed to measure the different importance of
job requirements for semantic representation, as well as measuring the
different contribution of each job experience to a specific ability
requirement. Finally, extensive experiments on a large scale real world data
set clearly validate the effectiveness and interpretability of the APJFNN
framework compared with several baselines.

本文提出了一种新颖的端到端能力感知人岗匹配神经网络模型，旨在利用过去的招聘数据创建单词级别的语义表示，并通过四种关注策略来测量语义表示的不同重要性，以及每个职业经验对特定能力要求的不同贡献，该模型通过大规模真实数据集的实验验证了它的有效性和可解释性。

提升人才招聘的人 - 职匹配性：一种基于能力感知神经网络的方法

Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural  Network Approach

Person-Job Fit is the process of matching the right talent for the right job
by identifying talent competencies that are required for the job. While many
qualitative efforts have been made in related fields, it still lacks of
quantitative ways of measuring talent competencies as well as the job's talent
requirements. To this end, in this paper, we propose a novel end-to-end
data-driven model based on Convolutional Neural Network (CNN), namely
Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to
the requirements of a job. To be specific, PJFNN is a bipartite neural network
which can effectively learn the joint representation of Person-Job fitness from
historical job applications. In particular, due to the design of a hierarchical
representation structure, PJFNN can not only estimate whether a candidate fits
a job, but also identify which specific requirement items in the job posting
are satisfied by the candidate by measuring the distances between corresponding
latent representations. Finally, the extensive experiments on a large-scale
real-world dataset clearly validate the performance of PJFNN in terms of
Person-Job Fit prediction. Also, we provide effective data visualization to
show some job and talent benchmark insights obtained by PJFNN.

本文提出了一种新颖的基于卷积神经网络（CNN）的数据驱动模型，即 Person-Job Fit Neural Network（PJFNN），通过识别工作所需的人才能力，为工作匹配合适的人才资格，成功设计了层次化表示架构，通过测量相应潜在表征之间的距离来评估不仅是否适合候选人，而且还可以识别候选人满足作业公告中哪些具体要求项目，最终在大型实际数据集上进行的广泛实验清楚地验证了 PJFNN 在 Person-Job 匹配预测方面的性能。