Papers by Chunzhen Jin
Reusing Transferable Weight Increments for Low-resource Style Generation (2024.emnlp-main)
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| Challenge: | Text style transfer (TST) is crucial in natural language processing, aiming to endow text with a new style without altering its meaning. |
| Approach: | They propose a framework to use style features in weight increments to transfer low-resource styles effectively. |
| Outcome: | The proposed framework achieves remarkable performance across different backbones, achieving particularly effective results in low-resource scenarios. |