The increased digitisation of the labour market has given researchers, educators, and companies the means to analyse and better understand the labour market. However, labour market resources, although available in high volumes, tend to be unstructured, and as such, research towards methodologies for the identification, linking, and extraction of entities becomes more and more important. Against the backdrop of this quest for better labour market representations, resource constraints and the unavailability of large-scale annotated data cause a reliance on human domain experts. In this paper, we propose a novel method that relies on pretrained language models (PLMs), prompt tuning with rules (PTR) , and the structured multilingual ESCO taxonomy, to efficiently and cheaply generate large amounts of labeled data for learning a variety of downstream tasks for extracting structured information from unstructured labour market data. We demonstrate the effectiveness of prompt-based tuning of pre-trained language models (PLM) in labour market specific applications. Our results indicate that cost-efficient methods such as PTR and instruction tuning without exemplars can significantly increase the performance of PLMs on downstream labour market applications without introducing additional model layers, manual annotations, and data augmentation. 
 

Read the full study: "Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules"

This article contributes to the broader collection of external ESCO publications, showcasing the use of ESCO within various methodologies or its presentation in both European and International contexts. As ESCO becomes increasingly used in applications and research projects across Europe and beyond, it is valuable to collect such sources and share best practices by diverse stakeholders. Therefore, this collection of external publications strengthens the exchange of knowledge within the ESCO community and can contribute to mutual learning in the field of skills, occupations and qualifications among European and international actors. If you are interested in sharing your publication, please write to EMPL-ESCO-SECRETARIAT@ec.europa.eu