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        <title>From None to Severe: Predicting Severity in Movie Scripts</title>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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            <namePart type="given">Lucia</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.332</identifier>
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%0 Conference Proceedings
%T From None to Severe: Predicting Severity in Movie Scripts
%A Zhang, Yigeng
%A Shafaei, Mahsa
%A Gonzalez, Fabio
%A Solorio, Thamar
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhang-etal-2021-none-severe
%X 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.
%R 10.18653/v1/2021.findings-emnlp.332
%U https://aclanthology.org/2021.findings-emnlp.332
%U https://doi.org/10.18653/v1/2021.findings-emnlp.332
%P 3951-3956

Markdown (Informal)

[From None to Severe: Predicting Severity in Movie Scripts](https://aclanthology.org/2021.findings-emnlp.332) (Zhang et al., Findings 2021)

• From None to Severe: Predicting Severity in Movie Scripts (Zhang et al., Findings 2021)

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.