Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification
Heng Gong, Wei Bi, Xiaocheng Feng, Bing Qin, Xiaojiang Liu, Ting Liu
Abstract
Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress. Based on results from previous work, the performance bottleneck of current models lies in the stage of content planing (selecting and ordering salient content from the input). That is, performance drops drastically when an oracle content plan is replaced by a model-inferred one during surface realization. In this paper, we propose to enhance neural content planning by (1) understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning; (2) verifying the importance and ordering of the selected sequence of records with policy gradient. We evaluated our model on ROTOWIRE and MLB, two datasets on this task, and results show that our model outperforms existing systems with respect to content planning metrics.- Anthology ID:
- 2020.findings-emnlp.262
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2905–2914
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2020.findings-emnlp.262/
- DOI:
- 10.18653/v1/2020.findings-emnlp.262
- Cite (ACL):
- Heng Gong, Wei Bi, Xiaocheng Feng, Bing Qin, Xiaojiang Liu, and Ting Liu. 2020. Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2905–2914, Online. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification (Gong et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2020.findings-emnlp.262.pdf
- Data
- RotoWire