"FIJO": a French Insurance Soft Skill Detection Dataset

TL;DR

This paper introduces FIJO, a new public dataset of insurance job offers with soft skill annotations, and evaluates transformer-based models for skill detection using this dataset.

Contribution

The paper presents FIJO, a novel annotated dataset for soft skill detection in insurance job ads, and assesses transformer models' effectiveness on this domain.

Findings

Transformers achieve good token-wise performance on FIJO.

FIJO reveals specific challenges in soft skill detection.

Analysis of errors highlights future research directions.

Abstract

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.