Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Computation and Language
2019-04-25 v2 Artificial Intelligence
Computational Finance
Machine Learning
Abstract
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.
Cite
@article{arxiv.1810.01165,
title = {Semi-supervised Text Regression with Conditional Generative Adversarial Networks},
author = {Tao Li and Xudong Liu and Shihan Su},
journal= {arXiv preprint arXiv:1810.01165},
year = {2019}
}