论文标题
可控文本生成具有重点变化
Controllable Text Generation with Focused Variation
论文作者
论文摘要
这项工作介绍了重点变化网络（FVN），这是一种用于控制语言生成的新型模型。先前受控语言生成模型的主要问题范围从根据给定属性生成文本的难度到缺乏生成文本的多样性。 FVN通过学习每个属性内部属性的分离潜在空间来解决这些问题，这允许可控性和多样性，同时又产生流利的文本。我们在两个具有带注释的内容和样式的文本生成数据集上评估FVN，并显示通过自动和人类评估评估的最先进的性能。
This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts. FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text. We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.
