@inproceedings{fan-etal-2020-enhanced,
title = "An Enhanced Knowledge Injection Model for Commonsense Generation",
author = "Fan, Zhihao and
Gong, Yeyun and
Wei, Zhongyu and
Wang, Siyuan and
Huang, Yameng and
Jiao, Jian and
Huang, Xuanjing and
Duan, Nan and
Zhang, Ruofei",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.182",
doi = "10.18653/v1/2020.coling-main.182",
pages = "2014--2025",
abstract = "Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, experimental results show that our method significantly improves the performance on all the metrics.",
}
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<abstract>Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, experimental results show that our method significantly improves the performance on all the metrics.</abstract>
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%0 Conference Proceedings
%T An Enhanced Knowledge Injection Model for Commonsense Generation
%A Fan, Zhihao
%A Gong, Yeyun
%A Wei, Zhongyu
%A Wang, Siyuan
%A Huang, Yameng
%A Jiao, Jian
%A Huang, Xuanjing
%A Duan, Nan
%A Zhang, Ruofei
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F fan-etal-2020-enhanced
%X Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, experimental results show that our method significantly improves the performance on all the metrics.
%R 10.18653/v1/2020.coling-main.182
%U https://aclanthology.org/2020.coling-main.182
%U https://doi.org/10.18653/v1/2020.coling-main.182
%P 2014-2025
Markdown (Informal)
[An Enhanced Knowledge Injection Model for Commonsense Generation](https://aclanthology.org/2020.coling-main.182) (Fan et al., COLING 2020)
ACL
- Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, and Ruofei Zhang. 2020. An Enhanced Knowledge Injection Model for Commonsense Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2014–2025, Barcelona, Spain (Online). International Committee on Computational Linguistics.