Decoupling Style from Contents for Positive Text Reframing

The positive text reframing (PTR) task, where the goal is to generate a text that gives a positive perspective to a reader while preserving the original sense of the input text, has attracted considerable attention as one of the natural language generation (NLG). In the PTR task, large annotated pairs of datasets are not available and would be expensive and time-consuming to create. Therefore, how to interpret a diversity of contexts and generate a positive perspective from a small size of the training dataset is still an open problem. In this work, we propose a simple but effective Framework for Decoupling the sentiment Style from the Contents of the text (FDSC) for the PTR task. Different from the previous work on the PTR task that utilizes Pre-trained Language Models (PLM) to directly fine-tune the task-specific labeled dataset such as Positive Psychology Frames (PPF), our FDSC fine-tunes the model for the input sequence with two special symbols to decouple style from the contents. We apply contrastive learning to enhance the model that learns a more robust contextual representation. The experimental results on the PPF dataset, show that our approach outperforms baselines by fine-turning two popular Seq2Seq PLMs, BART and T5, and can achieve better text reframing. Our codes are available online ( https://github.com/codesedoc/FDSC ).