# Semi-supervised Text Regression with Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1810.01165 · HTML: https://mlsanity.com/paper/1810.01165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Augmenting High-dimensional Nonlinear Optimization with Conditional GANs](https://mlsanity.com/paper/2103.04748/) (2021)

## Abstract (arXiv)

Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting high-level representations. Finally we point out related datasets for experiments and future research directions.