From None to Severe: Predicting Severity in Movie Scripts
Not on the map. Why?
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
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
- 2016Recurrent Neural Network for Text Classification with Multi-Task Learningcited by 144
- 2017A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learningcited by 4
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 3.
8 papers.
- 2023Positive and Risky Message Assessment for Music Productscited by 6
- 2025Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguitycited by 4
- 2025FUTURE: Flexible Unlearning for Tree Ensemblecited by 3
- 2022Deep Architectures for Content Moderation and Movie Content Ratingcited by 2
- 2024Interpreting Themes from Educational Storiescited by 1
- 2024Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Modelcited by 1
- 2023Extraction and Summarization of Explicit Video Content using Multi-Modal Deep Learningcited by 0
- 2025Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attentioncited by 0
- 2025FUTURE: Flexible Unlearning for Tree Ensemble28 Aug 2025
- 2025Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity30 Jul 2025
- 2025Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention11 Mar 2025
- 2024Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Model12 Jun 2024
- 2024Interpreting Themes from Educational Stories8 Apr 2024
- 2023Extraction and Summarization of Explicit Video Content using Multi-Modal Deep Learning17 Nov 2023
- 2023Positive and Risky Message Assessment for Music Products18 Sep 2023
- 2022Deep Architectures for Content Moderation and Movie Content Rating8 Dec 2022