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        <title>Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems</title>
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        <namePart type="given">Felipe</namePart>
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        <namePart type="given">Antoni</namePart>
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    <abstract>This paper presents the results of the Ninth Conference on Machine Translation (WMT24) Shared Task “Translation into Low-Resource Languages of Spain”’. The task focused on the development of machine translation systems for three language pairs: Spanish-Aragonese, Spanish-Aranese, and Spanish-Asturian. 17 teams participated in the shared task with a total of 87 submissions. The baseline system for all language pairs was Apertium, a rule-based machine translation system that still performs competitively well, even in an era dominated by more advanced non-symbolic approaches. We report and discuss the results of the submitted systems, highlighting the strengths of both neural and rule-based approaches.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems
%A Sánchez-Martínez, Felipe
%A Perez-Ortiz, Juan Antonio
%A Galiano Jimenez, Aaron
%A Oliver, Antoni
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F sanchez-martinez-etal-2024-findings
%X This paper presents the results of the Ninth Conference on Machine Translation (WMT24) Shared Task “Translation into Low-Resource Languages of Spain”’. The task focused on the development of machine translation systems for three language pairs: Spanish-Aragonese, Spanish-Aranese, and Spanish-Asturian. 17 teams participated in the shared task with a total of 87 submissions. The baseline system for all language pairs was Apertium, a rule-based machine translation system that still performs competitively well, even in an era dominated by more advanced non-symbolic approaches. We report and discuss the results of the submitted systems, highlighting the strengths of both neural and rule-based approaches.
%U https://aclanthology.org/2024.wmt-1.57
%P 684-698

Markdown (Informal)

[Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems](https://aclanthology.org/2024.wmt-1.57) (Sánchez-Martínez et al., WMT 2024)

• Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems (Sánchez-Martínez et al., WMT 2024)

ACL

• Felipe Sánchez-Martínez, Juan Antonio Perez-Ortiz, Aaron Galiano Jimenez, and Antoni Oliver. 2024. Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems. In Proceedings of the Ninth Conference on Machine Translation, pages 684–698, Miami, Florida, USA. Association for Computational Linguistics.