Semi-Supervised Formality Style Transfer

Semi-Supervised Formality Style Transfer is a subtask in Natural Language Processing aimed at automatically converting the formality level of text by utilizing a limited amount of labeled data and a large amount of unlabeled data. This task enhances the model's ability to transfer between different levels of formality through semi-supervised learning methods, improving its generalization and robustness. It is widely applied in text normalization, machine translation, and human-computer interaction, among other areas, and holds significant practical value.