Evaluating Pixel Language Models on Non-Standardized Languages
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929627287519232 |
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| author | Muñoz-Ortiz, Alberto Blaschke, Verena Plank, Barbara |
| author_facet | Muñoz-Ortiz, Alberto Blaschke, Verena Plank, Barbara |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_09084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Pixel Language Models on Non-Standardized Languages Muñoz-Ortiz, Alberto Blaschke, Verena Plank, Barbara Computation and Language 68T50 I.2.7 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. |
| title | Evaluating Pixel Language Models on Non-Standardized Languages |
| topic | Computation and Language 68T50 I.2.7 |
| url | https://arxiv.org/abs/2412.09084 |