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.- Anthology ID:
- 2021.findings-emnlp.332
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3951–3956
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.332
- DOI:
- 10.18653/v1/2021.findings-emnlp.332
- Cite (ACL):
- Yigeng Zhang, Mahsa Shafaei, Fabio Gonzalez, and Thamar Solorio. 2021. From None to Severe: Predicting Severity in Movie Scripts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3951–3956, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- From None to Severe: Predicting Severity in Movie Scripts (Zhang et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.332.pdf
- Code
- ritual-uh/predicting-severity-in-movie-scripts