Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine-grained settings following smooth variations in affective dimensions while preserving the meaning of the original text. We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels. Then, we propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation. Extensive evaluation of the fine-tuned models suggests that including fine-grained emotion labels in the paraphrase task significantly improves the likelihood of obtaining high-quality paraphrases that reflect the desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.

翻译：复述生成（即paraphrasing）是自然语言处理中常见且重要的任务。情感复述是指在保留文本含义的同时改变其中蕴含的情感，该任务具有多种潜在应用场景，包括调节在线对话与防止网络欺凌。我们提出了一项新任务：沿情绪梯度进行细粒度情感复述，即在保留原始文本含义的前提下，依据情感维度的平滑变化，以细粒度方式改变复述文本的情感强度。我们通过为输入文本和目标文本添加细粒度情感标签，重构了多个广泛使用的复述数据集。随后，我们提出了一种基于预训练语言模型的情感与情绪引导复述框架，用于条件文本生成。对微调模型的全面评估表明，在复述任务中加入细粒度情感标签显著提升了生成高质量复述文本的概率——这些文本既能反映目标情感，又能持续获得更优的复述指标分数（如BLEU、ROUGE和METEOR）。