# Fine-grained Sentiment Controlled Text Generation

arXiv: https://arxiv.org/abs/2006.09891 · HTML: https://mlsanity.com/paper/2006.09891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [Lexical Complexity Controlled Sentence Generation](https://mlsanity.com/paper/2211.14540/) (2022)
- [Evaluating Human-LLM Representation Alignment: A Case Study on Affective Sentence Generation for Augmentative and Alternative Communication](https://mlsanity.com/paper/2503.11881/) (2025)

## Abstract (arXiv)

Controlled text generation techniques aim to regulate specific attributes (e.g. sentiment) while preserving the attribute independent content. The state-of-the-art approaches model the specified attribute as a structured or discrete representation while making the content representation independent of it to achieve a better control. However, disentangling the text representation into separate latent spaces overlooks complex dependencies between content and attribute, leading to generation of poorly constructed and not so meaningful sentences. Moreover, such an approach fails to provide a finer control on the degree of attribute change. To address these problems of controlled text generation, in this paper, we propose DE-VAE, a hierarchical framework which captures both information enriched entangled representation and attribute specific disentangled representation in different hierarchies. DE-VAE achieves better control of sentiment as an attribute while preserving the content by learning a suitable lossless transformation network from the disentangled sentiment space to the desired entangled representation. Through feature supervision on a single dimension of the disentangled representation, DE-VAE maps the variation of sentiment to a continuous space which helps in smoothly regulating sentiment from positive to negative and vice versa. Detailed experiments on three publicly available review datasets show the superiority of DE-VAE over recent state-of-the-art approaches.