  This paper proposes a classification framework aimed at identifying
correlations between job ad requirements and transversal skill sets, with a
focus on predicting the necessary skills for individual job descriptions using
a deep learning model. The approach involves data collection, preprocessing,
and labeling using ESCO (European Skills, Competences, and Occupations)
taxonomy. Hierarchical classification and multi-label strategies are used for
skill identification, while augmentation techniques address data imbalance,
enhancing model robustness. A comparison between results obtained with
English-specific and multi-language sentence embedding models reveals close
accuracy. The experimental case studies detail neural network configurations,
hyperparameters, and cross-validation results, highlighting the efficacy of the
hierarchical approach and the suitability of the multi-language model for the
diverse European job market. Thus, a new approach is proposed for the
hierarchical classification of transversal skills from job ads.

Machine Learning;Computation and Language