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        <title>CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval</title>
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    <name type="personal">
        <namePart type="given">Katharina</namePart>
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        <namePart type="given">Andrei-Alexandru</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
%A Hämmerl, Katharina
%A Manea, Andrei-Alexandru
%A Vico, Gianluca
%A Helcl, Jindřich
%A Libovický, Jindřich
%Y Sälevä, Jonne
%Y Owodunni, Abraham
%S Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hammerl-etal-2024-cuni
%X 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.
%U https://aclanthology.org/2024.mrl-1.29
%P 357-364

Markdown (Informal)

[CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval](https://aclanthology.org/2024.mrl-1.29) (Hämmerl et al., MRL 2024)

• CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval (Hämmerl et al., MRL 2024)

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.