English

From None to Severe: Predicting Severity in Movie Scripts

Computation and Language 2021-10-05 v2

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.

Cite

@article{arxiv.2109.09276,
  title  = {From None to Severe: Predicting Severity in Movie Scripts},
  author = {Yigeng Zhang and Mahsa Shafaei and Fabio Gonzalez and Thamar Solorio},
  journal= {arXiv preprint arXiv:2109.09276},
  year   = {2021}
}

Comments

Accepted at Findings of EMNLP 2021

R2 v1 2026-06-24T06:07:24.694Z