Data2Text Studio: Automated Text Generation from Structured Data - Dou, Longxu; Qin, Guanghui; Wang, Jinpeng; Yao, Jin-Ge; Lin, Chin-Yew (Empirical Methods in Natural Language Processing, 2018)
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.