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Long Text Generation via Adversarial Training with Leaked Information

AAAI 2018 · Image generation › GANs · approach: GANs for text generation · 421 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use leaked discriminator features with a hierarchical MANAGER–WORKER generator instead of RankGANLeakGAN · Synthetic data, length 20NLL: 8.247 -> 7.038Helped
Use leaked discriminator features with a hierarchical MANAGER–WORKER generator instead of RankGANLeakGAN · Synthetic data, length 40NLL: 9.958 -> 7.191Helped
Use LeakGAN instead of the strongest baselineLeakGAN · EMNLP2017 WMT NewsBLEU-2: 0.8590 -> 0.956Helped
Use LeakGAN instead of the strongest baselineLeakGAN · EMNLP2017 WMT NewsBLEU-3: 0.6015 -> 0.819Helped
Use LeakGAN instead of the strongest baselineLeakGAN · EMNLP2017 WMT NewsBLEU-4: 0.4541 -> 0.627Helped
Use LeakGAN instead of the strongest baselineLeakGAN · EMNLP2017 WMT NewsBLEU-5: 0.463 -> 0.498Helped
Use LeakGAN instead of RankGANLeakGAN · COCO Image CaptionsBLEU-2: 0.850 -> 0.950Helped
Use LeakGAN instead of RankGANLeakGAN · COCO Image CaptionsBLEU-3: 0.672 -> 0.880Helped
Use LeakGAN instead of RankGANLeakGAN · COCO Image CaptionsBLEU-4: 0.557 -> 0.778Helped
Use LeakGAN instead of RankGANLeakGAN · COCO Image CaptionsBLEU-5: 0.544 -> 0.686Helped
Use LeakGAN instead of RankGANLeakGAN · Chinese PoemsBLEU-2: 0.812 -> 0.881Helped
Use LeakGAN instead of SeqGANLeakGAN · WMT NewsTuring test score: 0.236 -> 0.554Helped

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