CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
Katharina Hämmerl, Andrei-Alexandru Manea, Gianluca Vico, Jindřich Helcl, Jindřich Libovický
Abstract
We present the joint CUNI and LMU submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval.The shared task objective was to explore how we can deploy modern methods in NLP in multi-lingual low-resource settings, tested on two sub-tasks: Named-entity recognition and question answering.Our solutions to the subtasks are based on data acquisition and model adaptation.We compare the performance of our submitted systems with the translate-test approachwhich proved to be the most useful in the previous edition of the shared task.Our results show that using more data as well as fine-tuning recent multilingual pre-trained models leads to considerable improvements over the translate-test baseline.Our code is available at https://github.com/ufal/mrl2024-multilingual-ir-shared-task.- Anthology ID:
- 2024.mrl-1.29
- Volume:
- Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Jonne Sälevä, Abraham Owodunni
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 357–364
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.mrl-1.29/
- DOI:
- 10.18653/v1/2024.mrl-1.29
- Cite (ACL):
- Katharina Hämmerl, Andrei-Alexandru Manea, Gianluca Vico, Jindřich Helcl, and Jindřich Libovický. 2024. CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval. In Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024), pages 357–364, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval (Hämmerl et al., MRL 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.mrl-1.29.pdf