@InProceedings{dou-EtAl:2018:Demos,
  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 = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {13--18},
  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.},
  url       = {http://www.aclweb.org/anthology/D18-2003}
}