A Quality-Diversity Controllable GAN for Text Generation

ICLR 2020 Anonymous

Text generation is a critical and difficult natural language processing task. Maximum likelihood estimate (MLE) based models have been arguably suffered from exposure bias in the inference stage and thus varieties of language generative adversarial networks (GANs) bypassing this problem have emerged... (read more)

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