@inproceedings{dou2018data,
author = {Dou, Longxu and Qin, Guanghui and Wang, Jinpeng and Yao, Jin-Ge and Lin, Chin-Yew},
title = {Data2Text Studio: Automated Text Generation from Structured Data},
booktitle = {Empirical Methods in Natural Language Processing},
year = {2018},
month = {November},
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.},
publisher = {Association for Computational Linguistics},
url = {http://approjects.co.za/?big=en-us/research/publication/data2text-studio-automated-text-generation-from-structured-data/},
pages = {13-18},
}