EMNLP 2020
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.
This repository currently contains a streamlit app to run the tag predictor model. We will add more code soon.
run sh init.sh in terminal
cd demo_app
streamlit run app.py
@inproceedings{kar-etal-2020-multi,
title = "Multi-view Story Characterization from Movie Plot Synopses and Reviews",
author = "Kar, Sudipta and
Aguilar, Gustavo and
Lapata, Mirella and
Solorio, Thamar",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.454",
doi = "10.18653/v1/2020.emnlp-main.454",
pages = "5629--5646",
}
- For any queries, please contact the first author at skar3 AT uh DOT edu