Generating Text through Adversarial Training using Skip-Thought Vectors

NAACL 2019 Afroz Ahamad

In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Text Generation CMU-SE STWGAN-GP BLEU-3 0.617 # 1