MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations
MELO offers 48 datasets to evaluate entity linking of occupations across 21 languages using AI models. This benchmark helps standardize multilingual HR data normalization, supporting organizations in streamlining processes and improving global hiring.
Intent Classification Methods for Human Resources Chatbots
This paper analyzes intent classification techniques applied to HR chatbots. Besides exploring supervised and unsupervised learning methods, the study proposes a two-stage retrieval pipeline that increases performance and flexibility by allowing the addition of new intents without retraining.
Inductive Graph Neural Network for Job-Skill Framework Analysis
This study showcases the power of combining text encoding and graph neural networks to analyze job-skill links, enabling more accurate job and skill recommendations. It excels with structured and unstructured data, even when dealing with unseen skills.
Combined Unsupervised and Contrastive Learning for Multilingual Job Recommendation
This study explores a multilingual job recommendation model that improves ranking accuracy across 11 languages. Its two-stage learning approach supports cross-lingual recommendations and outperforms existing methods in aligning job titles globally.