Multi-view Story Characterization from Movie Plot Synopses and Reviews

Sudipta Kar, Gustavo Aguilar, Mirella Lapata, Thamar Solorio


Abstract
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.
Anthology ID:
2020.emnlp-main.454
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5629–5646
Language:
URL:
https://aclanthology.org/2020.emnlp-main.454
DOI:
10.18653/v1/2020.emnlp-main.454
Bibkey:
Cite (ACL):
Sudipta Kar, Gustavo Aguilar, Mirella Lapata, and Thamar Solorio. 2020. Multi-view Story Characterization from Movie Plot Synopses and Reviews. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5629–5646, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-view Story Characterization from Movie Plot Synopses and Reviews (Kar et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.454.pdf
Video:
 https://slideslive.com/38938754