SkillSpan: Hard and Soft Skill Extraction from English Job Postings

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4962 - 4984 July 10-15, 2022 ©2022 Association for Computational Linguistics SKILLSPAN: Hard and Soft Skill Extraction from English Job Postings Mike Zhang*} Kristian Nørgaard Jensen*} Sif Dam Sonniks} Barbara Plank}| }Department of Computer Science, IT University of Copenhagen, Denmark |Center for Information and Language Processing (CIS), LMU Munich, Germany {mikz, krnj, sifs}@itu.dk bplank@cis.uni-muenchen.de Abstract Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd- sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning. 1 Introduction Job markets are under constant development— often due to developments in technology, migra- tion, and digitization—so are the skill sets required. Consequentially, job vacancy data is emerging on a variety of platforms in big quantities and can pro- vide insights on labor market skill demands or aid job matching (Balog et al., 2012). SE is to extract the competences necessary from unstructured text. Previous work in SE shows promising progress, but is halted by a lack of available datasets and annotation guidelines. Two out of 14 studies release their dataset, which limit themselves to crowd-sourced labels (Sayfullina et al., 2018) or annotations from a predefined list of skills on the document-level (Bhola et al., 2020). Additionally, *Equal contribution. You will thrive working in a Dev/Sec Ops culture . SKILL KNOWLEDGE The ability to manage large sections of guests . SKILL Knowledge of property law rules of Germany . KNOWLEDGE Figure 1: Examples of Skills & Knowledge Compo- nents. Annotated samples of passages in varying job postings. More details are given in Section 4. none of the 14 previously mentioned studies re- lease their annotation guidelines, which obscures the meaning of a competence. Job markets change, as do the skills in, e.g., the European Skills, Com- petences, Qualifications and Occupations (ESCO; le Vrang et al., 2014) taxonomy (Section 3). Hence, it is important to cover for possible emerging skills. We propose SKILLSPAN, a novel SE dataset an- notated at the span-level for skill and knowledge components (SKCs) in job postings (JPs). As il- lustrated in Figure 1, SKCs can be nested inside skills. SKILLSPAN allows for extracting possibly undiscovered …

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