Evaluating Pixel Language Models on Non-Standardized Languages

MCML Authors

Verena Blaschke

→ Group Barbara Plank
 AI and Computational Linguistics

Barbara Plank

Prof. Dr.

Principal Investigator

AI and Computational Linguistics

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.

inproceedings

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COLING 2025

The 31st International Conference on Computational Linguistics. Abu Dhabi, United Arab Emirates, Jan 19-24, 2025.

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Authors

A. Muñoz-Ortiz • V. Blaschke • B. Plank

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Links

URL

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Research Area

 B2 | Natural Language Processing