# Emotion and Sentiment Guided Paraphrasing

arXiv: https://arxiv.org/abs/2306.05556 · HTML: https://mlsanity.com/paper/2306.05556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Automatically Neutralizing Subjective Bias in Text](https://mlsanity.com/paper/1911.09709/) (2019)
- [Paraphrase Generation with Deep Reinforcement Learning](https://mlsanity.com/paper/1711.00279/) (2017)
- [A Deep Generative Framework for Paraphrase Generation](https://mlsanity.com/paper/1709.05074/) (2017)
- [Neural Paraphrase Generation with Stacked Residual LSTM Networks](https://mlsanity.com/paper/1610.03098/) (2016)
- ["Mask and Infill" : Applying Masked Language Model to Sentiment Transfer](https://mlsanity.com/paper/1908.08039/) (2019)
- [Inducing Positive Perspectives with Text Reframing](https://mlsanity.com/paper/2204.02952/) (2022)

## Built on by (most cited first, 2 of 2)

- [Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues](https://mlsanity.com/paper/2412.20264/) (2024)
- [Conversation Derailment Forecasting with Graph Convolutional Networks](https://mlsanity.com/paper/2306.12982/) (2023)

## Abstract (arXiv)

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.