Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Tao Li; Xudong Liu; Shihan Su

doi:10.1109/bigdata.2018.8622140

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arXiv · 1810.01165

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Tao Li·Xudong Liu·Shihan Su

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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.

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Tao Li, Xudong Liu, Shihan Su. 2018-10-02. Semi-supervised Text Regression with Conditional Generative Adversarial Networks. https://doi.org/10.1109/bigdata.2018.8622140

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