Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Abstract
Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.- Anthology ID:
- 2020.acl-main.101
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1072–1086
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.101
- DOI:
- 10.18653/v1/2020.acl-main.101
- Cite (ACL):
- Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, and Changyou Chen. 2020. Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1072–1086, Online. Association for Computational Linguistics.
- Cite (Informal):
- Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (Wang et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2020.acl-main.101.pdf
- Data
- Wikipedia Person and Animal Dataset