Emotion and Sentiment Guided Paraphrasing
Justin J. Xie Ameeta Agrawal
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.