Integrating External Event Knowledge for Script Learning

Shangwen Lv, Fuqing Zhu, Songlin Hu


Abstract
Script learning aims to predict the subsequent event according to the existing event chain. Recent studies focus on event co-occurrence to solve this problem. However, few studies integrate external event knowledge to solve this problem. With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event. In this work, we integrate event knowledge from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event. We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage. In the knowledge retrieval stage, we select relevant external event knowledge from ASER. In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers. Experiments on the widely-used Multi- Choice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.
Anthology ID:
2020.coling-main.27
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:
306–315
Language:
URL:
https://aclanthology.org/2020.coling-main.27
DOI:
10.18653/v1/2020.coling-main.27
Bibkey:
Cite (ACL):
Shangwen Lv, Fuqing Zhu, and Songlin Hu. 2020. Integrating External Event Knowledge for Script Learning. In Proceedings of the 28th International Conference on Computational Linguistics, pages 306–315, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Integrating External Event Knowledge for Script Learning (Lv et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.27.pdf