@inproceedings{chen-etal-2019-ensuring,
title = "Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation",
author = "Chen, Jiangjie and
Wang, Ao and
Jiang, Haiyun and
Feng, Suo and
Li, Chenguang and
Xiao, Yanghua",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1196",
doi = "10.18653/v1/P19-1196",
pages = "2036--2046",
abstract = "A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.",
}
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<abstract>A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.</abstract>
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%0 Conference Proceedings
%T Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
%A Chen, Jiangjie
%A Wang, Ao
%A Jiang, Haiyun
%A Feng, Suo
%A Li, Chenguang
%A Xiao, Yanghua
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-ensuring
%X A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.
%R 10.18653/v1/P19-1196
%U https://aclanthology.org/P19-1196
%U https://doi.org/10.18653/v1/P19-1196
%P 2036-2046
Markdown (Informal)
[Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation](https://aclanthology.org/P19-1196) (Chen et al., ACL 2019)
ACL