Plan-then-Generate: Controlled Data-to-Text Generation via Planning
Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, Nigel Collier
Abstract
Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.
BibTeX
@inproceedings{su-etal-2021-plan-generate,
title = "Plan-then-Generate: Controlled Data-to-Text Generation via Planning",
author = "Su, Yixuan and
Vandyke, David and
Wang, Sihui and
Fang, Yimai and
Collier, Nigel",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.76/",
doi = "10.18653/v1/2021.findings-emnlp.76",
pages = "895--909"
}