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CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-Lingual Multi-Task Information Retrieval

MCML Authors

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 approach which 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.

inproceedings HMV+24

MRL @EMNLP 2024

4th Multilingual Representation Learning Workshop at the Conference on Empirical Methods in Natural Language Processing. Miami, FL, USA, Nov 12-16, 2024.

Authors

K. Hämmerl • A. Manea • G. Vico • J. Helcl • J. Libovický

Links

DOI

Research Area

 B2 | Natural Language Processing

BibTeXKey: HMV+24

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