论文标题
Optigan：目标优化序列生成的生成对抗网络
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
论文作者
论文摘要
序列生成任务中具有挑战性的问题之一是具有特定期望目标的优化生成序列。当前的顺序生成模型主要生成序列，以密切模仿训练数据，而无需直接优化任务特定的目标或属性。我们介绍了Optigan，这是一种生成模型，既结合了生成的对抗网络（GAN）和增强学习（RL），以使用策略梯度来优化所需的目标得分。我们将模型应用于文本和实价序列的生成，我们的模型能够获得更高的所需分数超过表现的GAN和RL基准，同时又不牺牲输出样品多样性。
One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly generate sequences to closely mimic the training data, without direct optimization of desired goals or properties specific to the task. We introduce OptiGAN, a generative model that incorporates both Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to optimize desired goal scores using policy gradients. We apply our model to text and real-valued sequence generation, where our model is able to achieve higher desired scores out-performing GAN and RL baselines, while not sacrificing output sample diversity.
