AggGen: Ordering and Aggregating while Generating
Xinnuo Xu, Ondřej Dušek, Verena Rieser, Ioannis Konstas
Abstract
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.
BibTeX
@inproceedings{xu-etal-2021-agggen,
title = "{A}gg{G}en: Ordering and Aggregating while Generating",
author = "Xu, Xinnuo and
Du{\v{s}}ek, Ond{\v{r}}ej and
Rieser, Verena and
Konstas, Ioannis",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.113/",
doi = "10.18653/v1/2021.acl-long.113",
pages = "1419--1434"
}