From None to Severe: Predicting Severity in Movie Scripts

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 1 .

Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels

“…It is an important task for various downstream applications like automatically assigning appropriate voices to utterances in audiobook production (Pan et al, 2021) and novel-to-script conversion (Soo et al, 2019). As dialogues serve as the major interaction between characters in novels, automatic identification of speakers can also be useful for novel-based knowledge mining tasks such as social network extraction (Jia et al, 2020) and personality profiling of the characters (Sang et al, 2022). Table 1 shows an example randomly sampled from the Chinese novel World of Plainness.…”