An Enhanced Knowledge Injection Model for Commonsense Generation
Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
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.- Anthology ID:
- 2020.coling-main.182
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2014–2025
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.coling-main.182/
- DOI:
- 10.18653/v1/2020.coling-main.182
- Cite (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.
- Cite (Informal):
- An Enhanced Knowledge Injection Model for Commonsense Generation (Fan et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.coling-main.182.pdf
- Data
- CommonGen, CommonsenseQA, SWAG