Data2Text Studio: Automated Text Generation from Structured Data
Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin
Abstract
Data2Text Studio is a platform for automated text generation from structured data. It is equipped with a Semi-HMMs model to extract high-quality templates and corresponding trigger conditions from parallel data automatically, which improves the interactivity and interpretability of the generated text. In addition, several easy-to-use tools are provided for developers to edit templates of pre-trained models, and APIs are released for developers to call the pre-trained model to generate texts in third-party applications. We conduct experiments on RotoWire datasets for template extraction and text generation. The results show that our model achieves improvements on both tasks.- Anthology ID:
- D18-2003
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13–18
- Language:
- URL:
- https://aclanthology.org/D18-2003
- DOI:
- 10.18653/v1/D18-2003
- Cite (ACL):
- Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, and Chin-Yew Lin. 2018. Data2Text Studio: Automated Text Generation from Structured Data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 13–18, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Data2Text Studio: Automated Text Generation from Structured Data (Dou et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/D18-2003.pdf