Learning to generate fluent natural language from structured data with neural
networks has become an common approach for NLG. This problem can be challenging
when the form of the structured data varies between examples. This paper
presents a survey of several extensions to sequence-to-sequence models to
account for the latent content selection process, particularly variants of copy
attention and coverage decoding. We further propose a training method based on
diverse ensembling to encourage models to learn distinct sentence templates
during training. An empirical evaluation of these techniques shows an increase
in the quality of generated text across five automated metrics, as well as
human evaluation.

本文介绍了多种扩展序列 - 序列模型的方法，旨在解决从结构化数据中生成流畅自然语言的问题，特别关注潜在内容选择过程的变体，包括复制注意力和覆盖解码。我们提出了一种基于多样集成的训练方法，以鼓励模型在训练过程中学习不同的句子模板，并通过比较其生成的文本在五个自动度量标准和人类评估中的结果，表明这些技术可以提高生成文本的质量。