EMNLP 2021finding7 citations
From None to Severe: Predicting Severity in Movie Scripts
Yigeng Zhang, Mahsa Shafaei, Fabio Gonzalez, Thamar Solorio
Abstract
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.
BibTeX
@inproceedings{zhang-etal-2021-none-severe,
title = "From None to Severe: {P}redicting Severity in Movie Scripts",
author = "Zhang, Yigeng and
Shafaei, Mahsa and
Gonzalez, Fabio and
Solorio, Thamar",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.332/",
doi = "10.18653/v1/2021.findings-emnlp.332",
pages = "3951--3956"
}