Journal
INFORMATION SCIENCES
Volume 450, Issue -, Pages 301-315Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.03.050
Keywords
Category sentence generation; Generative adversarial networks; Generative models; Supervised learning
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Funding
- Young Scientists Fund of the National Natural Science Foundation of China [61402373]
- Aeronautical Science Foundation of China Key Laboratory Project [20155553036]
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The neural network model has been the fulcrum of the so-called Al revolution. Although very powerful for pattern-recognition tasks, however, the model has two main drawbacks: it tends to overfit when the training dataset is small, and it is unable to accurately capture category information when the class number is large. In this paper, we combine reinforcement learning, generative adversarial networks, and recurrent neural networks to build a new model, termed category sentence generative adversarial network (CS-GAN). Not only the proposed model is able to generate category sentences that enlarge the original dataset, but also it helps improve its generalization capability during supervised training. We evaluate the performance of CS-GAN for the task of sentiment analysis. Quantitative evaluation exhibits the accuracy improvement in polarity detection on a small dataset with high category information. (C) 2018 Elsevier Inc. All rights reserved.
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