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:

Last modified October 28, 2024: Updates on data (c614c8b)