TalentCLEF: Skill and Job Title Intelligence for Human Capital Management
TalentCLEF aims to drive technological advancement in Human Capital Management by establishing a public benchmark for NLP models that facilitates their application in real-world Human Resources (HR) scenarios, incorporating evaluation criteria incluiding multilingualism, fairness, and cross-industry adaptability. The lab also seeks to build a community for researchers and practitioners to generate, evaluate, and discuss ideas on the use of AI in Human Resources, pushing the state-of-the-art of NLP applications for Human Resources.
Tasks:
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Task 1 - Multilingual Job Title Matching.
Task A involves the development of systems that can identify and rank job titles most similar to a given one. For each job title in a provided test set, participants must generate a ranked list of similar job titles from a specified knowledge base. The task includes multilingual and cross-lingual tracks, requiring participants to develop systems adapted to English, Spanish, German, and optionally Chinese.
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Task 2 - Job Title-Based Skill Prediction
Task B involves developing systems capable of retrieving relevant skills associated with a given job title. Participants must train models that can retrieve a list of relevant skills from a provided knowledge base, ranking them according to their relevance to the job title. This task is in English.
Organizers
- Luis Gascó - Avature Spain
- Hermenegildo Fabretat - Avature Spain
- Laura García-Sardiña - Avature Spain
- Álvaro Rodrigo - UNED
- Rabiz Zbib - Avature Spain
Contact
- luisgascosanchez@gmail.com
- gildo.fabregat@gmail.com
- laura.garcia@avature.net
- alvarory@lsi.uned.es
