{"id":714688,"date":"2020-12-30T03:08:30","date_gmt":"2020-12-30T11:08:30","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=714688"},"modified":"2020-12-30T03:08:30","modified_gmt":"2020-12-30T11:08:30","slug":"data2text-studio-automated-text-generation-from-structured-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/data2text-studio-automated-text-generation-from-structured-data\/","title":{"rendered":"Data2Text Studio: Automated Text Generation from Structured Data"},"content":{"rendered":"

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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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