CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492863" target="_blank" >RIV/00216208:11320/24:10492863 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.mrl-1.29/" target="_blank" >https://aclanthology.org/2024.mrl-1.29/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)
ISBN
979-8-89176-184-1
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
357-364
Publisher name
Association for Computational Linguistics
Place of publication
Kerrville, TX, USA
Event location
Miami, FL, USA
Event date
Nov 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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