Textagon: Boosting Language Models with Theory-guided Parallel Representations (2025.acl-demo)
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| Challenge: | Pretrained language models do not account for the wide variety of available expert-generated language resources and lexicons that explicitly encode linguistic/domain knowledge. |
| Approach: | They propose a Python package for generating parallel representations for text based on predefined lexicons and selecting representations that provide the most information. |
| Outcome: | The proposed model can generate parallel representations of text based on predefined lexicons and select representations that provide the most information. |
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