Abstract
We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as "soft templates," which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines.
Abstract (translated)
提出了一种新的条件文本生成模型。它从传统的基于模板的文本生成技术中获得灵感，源代码提供内容（即说什么），模板影响如何说。在成功的编码器-解码器范例的基础上，它首先对给定输入文本的内容表示进行编码；为了生成输出，它将训练数据中的范例文本检索为“软模板”，然后使用软模板构造范例特定的解码器。我们评估了抽象文本总结和数据到文本生成的模型。实验结果表明，该模型具有很强的性能，优于可比基线。
URL
https://arxiv.org/abs/1904.04428
