2021
EMNLP
EMNLP 2021
From None to Severe: Predicting Severity in Movie Scripts
Abstract
AbstractIn 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.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Core Methods > Multi-Task Learning
Deep Learning > Learning Types > Metric Learning