Text generation is a challenging task for intelligent agents. Numerous research attempts have investigated the use of adversarial networks with word sequence-based generators. However, these approaches suffer from an unbalance between generator and discriminator causing overfitting due to the strength that the discriminator acquires by getting too precise in distinguishing what the generator is producing and what instead comes from the real dataset. In this paper, we investigate how to balance both generator and discriminator of a sequence-based text adversarial network exploiting: i) the contribution of global knowledge in the input of the adversarial network encoded by global word embeddings that are adapted to the context of the datasets in which they are utilized, and ii) the use of a self-attentive discriminator that slowly minimizes its loss function and thus enables the generator to get valuable feedback during the training process. Through an extensive evaluation on three datasets of short-, medium- and long-length text documents, the results computed using word-overlapping metrics show that our model outperforms four baselines. We also discuss the results of our model using readability metrics and the human perceived quality of the generated documents.

对于智能代理，文本生成是一项艰巨的任务。许多研究尝试研究了对抗网络与基于单词序列的生成器的结合使用。但是，这些方法会遭受生成器和鉴别器之间的不平衡而导致的过度拟合，这是由于鉴别器通过过于精确地区分生成器产生的内容和取自真实数据集的内容而获得的强度而导致过度拟合。在本文中，我们研究如何平衡利用基于序列的文本对抗网络的生成器和判别器：i）全局知识在对抗网络输入中的贡献，该对抗网络的输入由适应于上下文的全局词嵌入编码利用它们的数据集，以及ii）使用自我注意的鉴别器，以使其损失函数缓慢地最小化，从而使生成器在训练过程中获得有价值的反馈。通过对短，中和长文本文档的三个数据集的广泛评估，使用词重叠量度计算出的结果表明，我们的模型优于四个基准。我们还将使用可读性度量标准和生成文档的人类感知质量来讨论模型的结果。