论文标题
通过建模遥远的未来来改善对抗性文本生成
Improving Adversarial Text Generation by Modeling the Distant Future
论文作者
论文摘要
自动回归文本生成模型通常集中在本地流利度上，并可能在长期文本生成中引起语义含义不一致。此外，自动生成具有类似语义的单词是具有挑战性的，并且难以应用手工制作的语言规则。我们考虑了一种文本计划计划，并提出了一种基于模型的模仿学习方法来减轻上述问题。具体而言，我们提出了一个新型的指南网络，以更长的地平线将重点放在生成过程上，该过程可以协助下一个字预测并为发电机优化提供中间奖励。广泛的实验表明，所提出的方法可改善性能。
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.
