We present AGGGEN (pronounced 'again'), a data-to-text model which
re-introduces two explicit sentence planning stages into neural data-to-text
systems: input ordering and input aggregation. In contrast to previous work
using sentence planning, our model is still end-to-end: AGGGEN performs
sentence planning at the same time as generating text by learning latent
alignments (via semantic facts) between input representation and target text.
Experiments on the WebNLG and E2E challenge data show that by using fact-based
alignments our approach is more interpretable, expressive, robust to noise, and
easier to control, while retaining the advantages of end-to-end systems in
terms of fluency. Our code is available at this https URL

本研究提出了 AGGGEN，一种数据到文本模型，重新引入了两个明确的句子规划阶段到神经数据到文本系统中：输入排序和输入聚合。我们的模型仍然是端到端的： AGGGEN 通过学习输入表示和目标文本之间的潜在对准（通过语义事实）在生成文本的同时执行句子规划。在 WebNLG 和 E2E 挑战数据上的实验表明，通过使用基于事实的对齐，我们的方法更可解释，更具表达性，对噪声更具鲁棒性，更易于控制，同时保留端到端系统在流畅度方面的优势。