Narrative Modeling with Memory Chains and Semantic Supervision

Fei Liu, Trevor Cohn, Timothy Baldwin


Abstract
Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.
Anthology ID:
P18-2045
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
278–284
Language:
URL:
https://aclanthology.org/P18-2045
DOI:
10.18653/v1/P18-2045
Bibkey:
Cite (ACL):
Fei Liu, Trevor Cohn, and Timothy Baldwin. 2018. Narrative Modeling with Memory Chains and Semantic Supervision. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 278–284, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Narrative Modeling with Memory Chains and Semantic Supervision (Liu et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2045.pdf
Code
 liufly/narrative-modeling