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.- Anthology ID:
- 2021.findings-emnlp.76
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 895–909
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.76
- DOI:
- 10.18653/v1/2021.findings-emnlp.76
- Cite (ACL):
- Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, and Nigel Collier. 2021. Plan-then-Generate: Controlled Data-to-Text Generation via Planning. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 895–909, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Plan-then-Generate: Controlled Data-to-Text Generation via Planning (Su et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.findings-emnlp.76.pdf
- Code
- google-research-datasets/ToTTo + additional community code