Semi-supervised Text Regression with Conditional Generative Adversarial Networks

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.

[1]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[2]  Jian Wang,et al.  Cooperative Adaptive Cruise Control for a Platoon of Connected and Autonomous Vehicles considering Dynamic Information Flow Topology , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[3]  Trevor Cohn,et al.  A user-centric model of voting intention from Social Media , 2013, ACL.

[4]  Kevin Lin,et al.  Adversarial Ranking for Language Generation , 2017, NIPS.

[5]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[8]  Noah A. Smith,et al.  Predicting Risk from Financial Reports with Regression , 2009, NAACL.

[9]  Noah A. Smith,et al.  Movie Reviews and Revenues: An Experiment in Text Regression , 2010, NAACL.

[10]  Matt J. Kusner,et al.  GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution , 2016, ArXiv.

[11]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[12]  Augustus Odena,et al.  Semi-Supervised Learning with Generative Adversarial Networks , 2016, ArXiv.

[13]  Nello Cristianini,et al.  Tracking the flu pandemic by monitoring the social web , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[14]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[15]  Zhi Chen,et al.  Adversarial Feature Matching for Text Generation , 2017, ICML.

[16]  Svitlana Volkova,et al.  Inferring User Political Preferences from Streaming Communications , 2014, ACL.

[17]  Trevor Cohn,et al.  Predicting and Characterising User Impact on Twitter , 2014, EACL.

[18]  Srinivas Peeta,et al.  Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles by Factoring Communication-Related Constraints , 2018, Transportation Research Part C: Emerging Technologies.

[19]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[20]  Jost Tobias Springenberg,et al.  Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks , 2015, ICLR.

[21]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[22]  Thomas L. Griffiths,et al.  Semi-Supervised Learning with Trees , 2003, NIPS.

[23]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[24]  Zhe Gan,et al.  Generating Text via Adversarial Training , 2016 .

[25]  F. Rudzicz.  Human Language Technologies : The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics , 2010 .

[26]  Srinivas Peeta,et al.  Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints , 2019 .

[27]  Alan Ritter,et al.  Adversarial Learning for Neural Dialogue Generation , 2017, EMNLP.

[28]  Kaiming Fu,et al.  Toward Robust and Efficient Training of Generative Adversarial Networks with Bayesian Approximation , 2018 .

[29]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Trevor Cohn,et al.  Non-Linear Text Regression with a Deep Convolutional Neural Network , 2015, ACL.

[31]  Nikolaos Aletras,et al.  An analysis of the user occupational class through Twitter content , 2015, ACL.

[32]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[33]  Jian Wang,et al.  YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles , 2018, 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP).

[34]  Guodong Guo,et al.  Attributes in Multiple Facial Images , 2018, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).