@inproceedings{dou-etal-2018-data2text,
title = "{D}ata2{T}ext Studio: Automated Text Generation from Structured Data",
author = "Dou, Longxu and
Qin, Guanghui and
Wang, Jinpeng and
Yao, Jin-Ge and
Lin, Chin-Yew",
editor = "Blanco, Eduardo and
Lu, Wei",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-2003",
doi = "10.18653/v1/D18-2003",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Data2Text Studio: Automated Text Generation from Structured Data
%A Dou, Longxu
%A Qin, Guanghui
%A Wang, Jinpeng
%A Yao, Jin-Ge
%A Lin, Chin-Yew
%Y Blanco, Eduardo
%Y Lu, Wei
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F dou-etal-2018-data2text
%X 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.
%R 10.18653/v1/D18-2003
%U https://aclanthology.org/D18-2003
%U https://doi.org/10.18653/v1/D18-2003
%P 13-18
Markdown (Informal)
[Data2Text Studio: Automated Text Generation from Structured Data](https://aclanthology.org/D18-2003) (Dou et al., EMNLP 2018)
ACL
- Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, and Chin-Yew Lin. 2018. Data2Text Studio: Automated Text Generation from Structured Data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 13–18, Brussels, Belgium. Association for Computational Linguistics.