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Correct title and author name for 2024.wmt-1.89 #4077

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Correct title and author name for 2024.wmt-1.89 #4077

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4 changes: 2 additions & 2 deletions data/xml/2024.wmt.xml
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Expand Up @@ -1103,10 +1103,10 @@
<bibkey>de-gibert-etal-2024-hybrid</bibkey>
</paper>
<paper id="89">
<title>Robustness of Fine-Tuned <fixed-case>LLM</fixed-case>s for Machine Translation with Varying Noise Levels: Insights for <fixed-case>A</fixed-case>sturian, <fixed-case>A</fixed-case>ragonese and Aranese</title>
<title>Robustness of Fine-Tuned Models for Machine Translation with Varying Noise Levels: Insights for <fixed-case>A</fixed-case>sturian, <fixed-case>A</fixed-case>ragonese and Aranese</title>
<author><first>Martin</first><last>Bär</last><affiliation>University of the Basque Country</affiliation></author>
<author><first>Elisa</first><last>Forcada Rodríguez</last><affiliation>University of the Basque Country</affiliation></author>
<author><first>Maria</first><last>Garcia-Abadillo</last><affiliation>University of the Basque Country</affiliation></author>
<author><first>María</first><last>García-Abadillo Velasco</last><affiliation>University of the Basque Country</affiliation></author>
<pages>918-924</pages>
<abstract>We present the LCT-LAP proposal for the shared task on Translation into Low-Resource Languages of Spain at WMT24 within the constrained submission category. Our work harnesses encoder-decoder models pretrained on higher-resource Iberian languages to facilitate MT model training for Asturian, Aranese and Aragonese. Furthermore, we explore the robustness of these models when fine-tuned on datasets with varying levels of alignment noise. We fine-tuned a Spanish-Galician model using Asturian data filtered by BLEU score thresholds of 5, 15, 30 and 60, identifying BLEU 15 as the most effective. This threshold was then applied to the Aranese and Aragonese datasets. Our findings indicate that filtering the corpora reduces computational costs and improves performance compared to using nearly raw data or data filtered with language identification. However, it still falls short of the performance achieved by the rule-based system Apertium in Aranese and Aragonese.</abstract>
<url hash="df247c4c">2024.wmt-1.89</url>
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