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• W2892153332 abstract "Existing text generation methods tend to produce repeated and ”boring” expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines." @default.
• W2892153332 created "2018-09-27" @default.
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• W2892153332 date "2018-01-01" @default.
• W2892153332 modified "2023-10-11" @default.
• W2892153332 title "Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation" @default.
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• W2892153332 doi "https://doi.org/10.18653/v1/d18-1428" @default.
• W2892153332 hasPublicationYear "2018" @default.
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