COLING 2020main36 citations
An Enhanced Knowledge Injection Model for Commonsense Generation
Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan
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.
BibTeX
@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"
}