On the TalentCLEF 2025 benchmark, zero-shot prompting gave the best multilingual job-title matching result, while fine-tuned classification gave the best job-skill prediction result.
MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations
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abstract
We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research. The datasets and source code for standardized evaluation are publicly available at https://github.com/Avature/melo-benchmark
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NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
On the TalentCLEF 2025 benchmark, zero-shot prompting gave the best multilingual job-title matching result, while fine-tuned classification gave the best job-skill prediction result.