Additional resources

To support your participation in this shared task, we have compiled a list of additional resources that may be useful for understanding the task better, exploring related work, and utilizing domain-specific models.

  • Zbib, R., Lacasa, L. A., Retyk, F., Poves, R., Aizpuru, J., Fabregat, H., … & García-Casademont, E. (2022). Learning Job Titles Similarity from Noisy Skill Labels. arXiv preprint arXiv:2207.00494
  • Deniz, D., Retyk, F., García-Sardiña, L., Fabregat, H., Gasco, L., & Zbib, R. (2024). Combined Unsupervised and Contrastive Learning for Multilingual Job Recommendation. Link CEUR
  • Decorte, J. J., Van Hautte, J., Demeester, T., & Develder, C. (2021). Jobbert: Understanding job titles through skills. arXiv preprint arXiv:2109.09605
  • Anand, S., Decorte, J. J., & Lowie, N. (2022). Is it required? ranking the skills required for a job-title. arXiv preprint arXiv:2212.08553
  • Zhang, M., Van Der Goot, R., & Plank, B. (2023). ESCOXLM-R: Multilingual taxonomy-driven pre-training for the job market domain. arXiv preprint arXiv:2305.12092
  • Bhola, A., Halder, K., Prasad, A., & Kan, M. Y. (2020, December). Retrieving skills from job descriptions: A language model based extreme multi-label classification framework. In Proceedings of the 28th international conference on computational linguistics (pp. 5832-5842). Link
  • Retyk, F., Gasco, L., Carrino, C. P., Deniz, D., & Zbib, R. (2024). MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations. arXiv preprint arXiv:2410.08319.

2. External Resources:

3. Tutorials:

We will publish a series of notebooks covering the fundamentals, including how to work with the data and upload predictions to Codalab.

NotebookColab
Data Download and LoadOpen In Colab
Prepare submission file and run evaluationOpen In Colab
Task A - Development set Baseline generationOpen In Colab
Task B - Prepare submission file and run evaluationOpen In Colab
Last modified March 17, 2025: Update schedule taskB-dev set (be44960)