StyleLLM文风大模型:基于大语言模型的文本风格迁移项目。Text style transfer base on Large Language Model. #文字修饰 # 润色 #风格模仿
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Updated
Jun 3, 2024
StyleLLM文风大模型:基于大语言模型的文本风格迁移项目。Text style transfer base on Large Language Model. #文字修饰 # 润色 #风格模仿
This paper provides a systematic review of Text Style Transfer (TST), categorizing past works based on machine learning and deep learning algorithms, discussing various TST subtasks, compiling available datasets, summarizing evaluation practices, and highlighting current challenges and future research directions in TST.
A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
Learning Disentangled Meaning and Style Representations for Positive Text Reframing
Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch
Persona Styled Recipes are generated given a list of ingredients
About, prompt-based few-shot learning, Text Generation with Prompting
Multidimensional Evaluation for Text Style Transfer Using ChatGPT. Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer (HumEval 2022)
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Empathy Style Transformer with Discriminative mechanism
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer (ACL 2021)
Artistic Text Style Transfer for Complex Texture and Structure
Generic resources are what you need: Style transfer tasks without task-specific parallel training data (EMNLP 2021)
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (ACL 2022)
Dataset for evaluating the quality of content preservation measure in text formality transfer for task oriented domain
Presentation: https://docs.google.com/presentation/d/1p8rDXqzizx0ZXpz-hTmolJyTv0tgvYGn-KnwRIGP2Q8/edit?usp=sharing
A slick text style transfer framework.
[Attention] 텍스트 어체 변환기
Code for "Data-to-text Generation with Style Imitation." [Findings of EMNLP 2020]
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