mlsanityData to 7 Oct 2026

From None to Severe: Predicting Severity in Movie Scripts

arXiv 2109.09276 ↗First posted 20 Sep 2021cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

5 of 14 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  4. 2016Recurrent Neural Network for Text Classification with Multi-Task Learningcited by 144
  5. 2017A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learningcited by 4

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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 3.

8 papers.