# From None to Severe: Predicting Severity in Movie Scripts

arXiv: https://arxiv.org/abs/2109.09276 · HTML: https://mlsanity.com/paper/2109.09276/

## Key facts

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

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1707.02892/) (2017)

## Built on by (most cited first, 8 of 8)

- [Positive and Risky Message Assessment for Music Products](https://mlsanity.com/paper/2309.10182/) (2023)
- [Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity](https://mlsanity.com/paper/2507.23121/) (2025)
- [FUTURE: Flexible Unlearning for Tree Ensemble](https://mlsanity.com/paper/2508.21181/) (2025)
- [Deep Architectures for Content Moderation and Movie Content Rating](https://mlsanity.com/paper/2212.04533/) (2022)
- [Interpreting Themes from Educational Stories](https://mlsanity.com/paper/2404.05250/) (2024)
- [Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Model](https://mlsanity.com/paper/2406.07841/) (2024)
- [Extraction and Summarization of Explicit Video Content using Multi-Modal Deep Learning](https://mlsanity.com/paper/2311.10899/) (2023)
- [Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention](https://mlsanity.com/paper/2503.08079/) (2025)

## 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.