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
Editors:
Eduardo Blanco, Wei Lu
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D18-2003.pdf