Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model

Bingfeng Luo, Zuo Bai, Kunfeng Lai, Jianping Shen


Abstract
Neural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control the generation contents. Moreover, it requires paired training data that are usually expensive to collect. Template-based methods have good fidelity and controllability but require heavy human involvement. We propose a novel template-based data2text system powered by a text stitch model. It ensures fidelity and controllability by using templates to produce the main contents. In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability. The text stitch model can be trained in self-supervised fashion, which only requires free texts. The experiments on a benchmark dataset show that our system outperforms SOTA NN-based systems in fidelity and surpasses template-based systems in diversity and human involvement.
Anthology ID:
2020.findings-emnlp.94
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1057–1062
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.94
DOI:
10.18653/v1/2020.findings-emnlp.94
Bibkey:
Cite (ACL):
Bingfeng Luo, Zuo Bai, Kunfeng Lai, and Jianping Shen. 2020. Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1057–1062, Online. Association for Computational Linguistics.
Cite (Informal):
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model (Luo et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.94.pdf
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 2020.findings-emnlp.94.OptionalSupplementaryMaterial.zip