# Inducing Positive Perspectives with Text Reframing

arXiv: https://arxiv.org/abs/2204.02952 · HTML: https://mlsanity.com/paper/2204.02952/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 116 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 30 of 41)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [GeDi: Generative Discriminator Guided Sequence Generation](https://mlsanity.com/paper/2009.06367/) (2020)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [Style Transfer in Text: Exploration and Evaluation](https://mlsanity.com/paper/1711.06861/) (2017)
- [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Automatically Neutralizing Subjective Bias in Text](https://mlsanity.com/paper/1911.09709/) (2019)
- [Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer](https://mlsanity.com/paper/1908.09368/) (2019)
- [Politeness Transfer: A Tag and Generate Approach](https://mlsanity.com/paper/2004.14257/) (2020)
- [Evaluating Style Transfer for Text](https://mlsanity.com/paper/1904.02295/) (2019)
- [PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction](https://mlsanity.com/paper/2010.13816/) (2020)
- [Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models](https://mlsanity.com/paper/1707.01161/) (2017)

## Built on by (most cited first, 30 of 31)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025)
- [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023)
- [Benefits and Harms of Large Language Models in Digital Mental Health](https://mlsanity.com/paper/2311.14693/) (2023)
- [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023)
- [Can AI Relate: Testing Large Language Model Response for Mental Health Support](https://mlsanity.com/paper/2405.12021/) (2024)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided](https://mlsanity.com/paper/2404.01288/) (2024)
- [MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance](https://mlsanity.com/paper/2504.13211/) (2025)
- [Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Health](https://mlsanity.com/paper/2507.10695/) (2025)
- [Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models](https://mlsanity.com/paper/2310.14389/) (2023)
- [CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models](https://mlsanity.com/paper/2402.13109/) (2024)
- [Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues](https://mlsanity.com/paper/2504.17238/) (2025)
- [The Inner Sentiments of a Thought](https://mlsanity.com/paper/2307.01784/) (2023)
- [Defining a New NLP Playground](https://mlsanity.com/paper/2310.20633/) (2023)
- [Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing](https://mlsanity.com/paper/2409.19025/) (2024)
- [TalkUp: Paving the Way for Understanding Empowering Language](https://mlsanity.com/paper/2305.14326/) (2023)
- [How About Kind of Generating Hedges using End-to-End Neural Models?](https://mlsanity.com/paper/2306.14696/) (2023)
- [Promoting Constructive Deliberation: Reframing for Receptiveness](https://mlsanity.com/paper/2405.15067/) (2024)
- [Exploratory Study into Relations between Cognitive Distortions and Emotional Appraisals](https://mlsanity.com/paper/2503.15979/) (2025)
- [Emotion and Sentiment Guided Paraphrasing](https://mlsanity.com/paper/2306.05556/) (2023)
- ["Mistakes Help Us Grow": Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms](https://mlsanity.com/paper/2310.10637/) (2023)
- [Conversation Derailment Forecasting with Graph Convolutional Networks](https://mlsanity.com/paper/2306.12982/) (2023)
- [Task-Oriented Paraphrase Analytics](https://mlsanity.com/paper/2403.17564/) (2024)
- [Positive Text Reframing under Multi-strategy Optimization](https://mlsanity.com/paper/2407.17940/) (2024)
- [Fine-Grained Emotional Paraphrasing along Emotion Gradients](https://mlsanity.com/paper/2212.03297/) (2022)
- [Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management](https://mlsanity.com/paper/2406.19543/) (2024)
- [Formalizing Style in Personal Narratives](https://mlsanity.com/paper/2510.08649/) (2025)
- [StageWell: A Process-Aligned Chinese Corpus for Positive-Psychology Support Dialogue](https://mlsanity.com/paper/2608.29326/) (2026)

## Abstract (arXiv)

Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a reversal in meaning. We introduce a different but related task called positive reframing in which we neutralize a negative point of view and generate a more positive perspective for the author without contradicting the original meaning. Our insistence on meaning preservation makes positive reframing a challenging and semantically rich task. To facilitate rapid progress, we introduce a large-scale benchmark, Positive Psychology Frames, with 8,349 sentence pairs and 12,755 structured annotations to explain positive reframing in terms of six theoretically-motivated reframing strategies. Then we evaluate a set of state-of-the-art text style transfer models, and conclude by discussing key challenges and directions for future work.