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ARAML: A Stable Adversarial Training Framework for Text Generation

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Figure

Figure 1: Overview of ARAML. The training samples are acquired from a stationary distributionsample Train  P based on thessample real data
Table 1: Training objectives of generators for RAML,MaliGAN and ARAML.
Table 3: Implementation details of ARAML. G/D/LMindicates the generator / discriminator / language modelused in constrained sampling, respectively.
Table 4: Automatic evaluation on COCO and EMNLP2017 WMT. Each metric is presented with mean and standarddeviation.
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