Data2Text Studio: Automated Text Generation from Structured Data – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

Data2Text Studio: Automated Text Generation from Structured Data

NLPIR SEMINAR Y2019#28

INTRO

In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

Arrangement

Tomorrow’s seminar is organized as follows:

  1. The seminar time is 1.pm, Mon (September 1, 2019), at Zhongguancun Technology Park ,Building 5, 1306.
  2. Changhe Li is going to give a presentation, the paper’s title is Data2Text Studio: Automated Text Generation from Structured Data.
  3. The seminar will be hosted by Qinghong Jiang.

Everyone interested in this topic is welcomed to join us.
The following is the abstract of the paper.

Data2Text Studio: Automated Text Generation from Structured Data

Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, and 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.

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