Formality Style Transfer
10 papers with code • 1 benchmarks • 1 datasets
Formality Style Transfer
Benchmarks
These leaderboards are used to track progress in Formality Style Transfer
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
GYAFC
Most implemented papers
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer
fuzhenxin/Style-Transfer-in-Text
•
•
EMNLP 2021
While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation.
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Style transfer is the task of automatically transforming a piece of text in one particular style into another.
Parallel Data Augmentation for Formality Style Transfer
The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data.
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization
GT-SALT/FormalityStyleTransfer
•
•
Findings of the Association for Computational Linguistics 2020
Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences, which can be used to improve performance of many downstream NLP tasks.
Formality Style Transfer with Shared Latent Space
jimth001/formality_style_transfer_with_shared_latent_space
•
•
COLING 2020
Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training.
XFORMAL: A Benchmark for Multilingual Formality Style Transfer
We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian.
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
laihuiyuan/Pre-trained-formality-transfer
•
•
ACL 2021
Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content.
Semi-Supervised Formality Style Transfer with Consistency Training
In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training.
CoEdIT: Text Editing by Task-Specific Instruction Tuning
We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions).
ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer
We propose a framework to augment and improve a formality style transfer dataset with explanations via model distillation from ChatGPT.
