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.

本研究介绍了一种新的 fine-grained 情感改写任务，该任务能够通过平滑变化情感维度，保留原始文本的含义，从而改变带有情感的文本的情感强度。研究者提出了一种情感和情感指导的改写框架，该框架依靠预先训练的语言模型进行文本生成。经过广泛的评估，研究者发现，包含细粒度情感标签的改写任务可以显著提高获得反映期望情感的高质量改写的可能性，同时在改写评估指标（如 BLEU，ROUGE 和 METEOR）方面获得了一致性更好的得分。

情感和情绪引导的改写

Emotion and Sentiment Guided Paraphrasing

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, e.g., 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 grain following smooth variations in affective dimensions while preserving
the meanings of the originals. We propose a framework for addressing this task
by fine-tuning text-to-text Transformers through multi-task training. We
enhance several widely used paraphrasing corpus by annotating the input and
target texts with their fine-grained emotion labels. With these labels,
fine-tuning text-to-text Transformers on these corpus entails multi-task
training. Evaluations of the fine-tuned Transformers on separate test sets show
that including fine-grained emotion labels in the paraphrase task significantly
improve the chance of obtaining high-quality paraphrases of the desired
emotions, i.e., more than doubling the number of exact matches of desired
emotions while achieving consistently better scores in paraphrase metrics such
as BLEU, ROGUE, and METEOR.

提出一种新的基于感情梯度的细粒度情感改写任务，通过 fine-tuning 文本到文本的转换器进行多任务训练，对输入和目标文本进行细粒度情感标注，评估结果表明，将情感标签纳入改写任务可以显著提高获取所期望情感的高质量改写的几率，并在多个改写指标上达到更好的成绩。