Papers with style transfer accuracy
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization (2021.acl-long)
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| Challenge: | Text style transfer aims to alter the style of a sentence while preserving its content. |
| Approach: | They propose to remove style information at token level and fuse it to style representations using conditional layer normalization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in terms of content preservation and fluency. |
Text Style Transfer for Bias Mitigation using Masked Language Modeling (2022.naacl-srw)
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| Challenge: | Various research findings have concluded that biased textual data has significant effects on target demographic groups. |
| Approach: | They propose a text-style transfer model that can be trained on non-parallel data and be used to automatically mitigate bias in textual data. |
| Outcome: | The proposed model improves on limitations of existing methods while maintaining good style transfer accuracy. |
Politeness Transfer: A Tag and Generate Approach (2020.acl-main)
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Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
Text Style Transfer Evaluation Using Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated their capacity to match and even exceed average human performance across diverse, unseen tasks. |
| Approach: | They compare the results of different LLMs in TST evaluation using multiple input prompts and introduce the concept of prompt ensembling. |
| Outcome: | The proposed model outperforms human evaluations on multiple input prompts. |