A Simple and Effective Approach to the Story Cloze Test

Siddarth Srinivasan, Richa Arora, Mark Riedl


Abstract
In the Story Cloze Test, a system is presented with a 4-sentence prompt to a story, and must determine which one of two potential endings is the ‘right’ ending to the story. Previous work has shown that ignoring the training set and training a model on the validation set can achieve high accuracy on this task due to stylistic differences between the story endings in the training set and validation and test sets. Following this approach, we present a simpler fully-neural approach to the Story Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance on this task without any feature engineering. We also find that considering just the last sentence of the prompt instead of the whole prompt yields higher accuracy with our approach.
Anthology ID:
N18-2015
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
92–96
Language:
URL:
https://aclanthology.org/N18-2015
DOI:
10.18653/v1/N18-2015
Bibkey:
Cite (ACL):
Siddarth Srinivasan, Richa Arora, and Mark Riedl. 2018. A Simple and Effective Approach to the Story Cloze Test. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 92–96, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
A Simple and Effective Approach to the Story Cloze Test (Srinivasan et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2015.pdf
Note:
 N18-2015.Notes.pdf
Data
StoryCloze