Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation (D18-1)
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| Challenge: | Existing text generation methods tend to produce repeated and ”boring” expressions. |
| Approach: | They propose a model that assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, and a novel language-model based discriminator which can distinguish novel text from repeated text without the saturation problem. |
| Outcome: | The proposed model generates more diverse and informative text than existing baselines on review generation and dialogue generation tasks. |
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
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