  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 https://github.com/XinnuoXu/AggGen.
