Script Induction as Association Rule Mining

Anton Belyy, Benjamin Van Durme


Abstract
We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event. The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics. We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test.
Anthology ID:
2020.nuse-1.7
Volume:
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
Month:
July
Year:
2020
Address:
Online
Venue:
NUSE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
55–62
Language:
URL:
https://aclanthology.org/2020.nuse-1.7
DOI:
10.18653/v1/2020.nuse-1.7
Bibkey:
Cite (ACL):
Anton Belyy and Benjamin Van Durme. 2020. Script Induction as Association Rule Mining. In Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pages 55–62, Online. Association for Computational Linguistics.
Cite (Informal):
Script Induction as Association Rule Mining (Belyy & Van Durme, NUSE 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.nuse-1.7.pdf
Video:
 http://slideslive.com/38929746