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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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            <namePart type="given">Owen</namePart>
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    <abstract>We explore the potential of pixel-based models for transfer learning from standard languages to dialects. These models convert text into images that are divided into patches, enabling a continuous vocabulary representation that proves especially useful for out-of-vocabulary words common in dialectal data. Using German as a case study, we compare the performance of pixel-based models to token-based models across various syntactic and semantic tasks. Our results show that pixel-based models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. However, pixel-based models fall short in topic classification. These findings emphasize the potential of pixel-based models for handling dialectal data, though further research should be conducted to assess their effectiveness in various linguistic contexts.</abstract>
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%0 Conference Proceedings
%T Evaluating Pixel Language Models on Non-Standardized Languages
%A Muñoz-Ortiz, Alberto
%A Blaschke, Verena
%A Plank, Barbara
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F munoz-ortiz-etal-2025-evaluating
%X We explore the potential of pixel-based models for transfer learning from standard languages to dialects. These models convert text into images that are divided into patches, enabling a continuous vocabulary representation that proves especially useful for out-of-vocabulary words common in dialectal data. Using German as a case study, we compare the performance of pixel-based models to token-based models across various syntactic and semantic tasks. Our results show that pixel-based models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. However, pixel-based models fall short in topic classification. These findings emphasize the potential of pixel-based models for handling dialectal data, though further research should be conducted to assess their effectiveness in various linguistic contexts.
%U https://aclanthology.org/2025.coling-main.427/
%P 6412-6419

Markdown (Informal)

[Evaluating Pixel Language Models on Non-Standardized Languages](https://aclanthology.org/2025.coling-main.427/) (Muñoz-Ortiz et al., COLING 2025)

• Evaluating Pixel Language Models on Non-Standardized Languages (Muñoz-Ortiz et al., COLING 2025)

ACL

• Alberto Muñoz-Ortiz, Verena Blaschke, and Barbara Plank. 2025. Evaluating Pixel Language Models on Non-Standardized Languages. In Proceedings of the 31st International Conference on Computational Linguistics, pages 6412–6419, Abu Dhabi, UAE. Association for Computational Linguistics.