A dual-layered sentence and paragraph template method for zero-shot long-text style transfer, with a reported average gain of 0.20 over direct prompting but limited statistical and external support.
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
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abstract
Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.
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Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping
A dual-layered sentence and paragraph template method for zero-shot long-text style transfer, with a reported average gain of 0.20 over direct prompting but limited statistical and external support.