@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://preview.aclanthology.org/jlcl-multiple-ingestion/2021.findings-emnlp.332/",
    doi = "10.18653/v1/2021.findings-emnlp.332",
    pages = "3951--3956",
    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."
}