From None to Severe: Predicting Severity in Movie Scripts

Authors: Yigeng Zhang, Mahsa Shafaei, Fabio Gonzalez and Thamar Solorio

This repository hosts the dataset and the source code of the paper From None to Severe: Predicting Intensity in Movie Scripts.

Dataset

Please find the dataset under the Data folder.

This zip file contains train/dev/test files of 5 aspects of age-restricted content.

The data is formatted in the following columns in a Pandas Dataframe:

id | Aspect | None | Mild | Moderate | Severe | Total_votes | Aspect_rating | text

Code

Please find the code under the Code folder.

Dependency libraries

In this work, we use Python 3.7.9. The dependency libraries are with the following versions:

NumPy 1.18.5
Pandas 1.1.3
PyTorch 1.6.0
PyTorch Lightning 1.0.2
Scikit-learn 0.23.2
Sentence-transformers 0.4.1.2

Use of the code

SentenceBERT embedding

Use the following command to embed all text into utterance-level sentence embeddings.

python text_embedding.py --data_dir your-save-path

The reason for obtaining and saving sentence embeddings beforehand is to reuse and save experiment time. Otherwise getting embeddings together with training will drastically increase the running time.

Training and testing the model

Use the following command to run the training-test script with default settings.

python RNN-Trans_S-MT.py
python TextRCNN_S-MT.py

To test the code correctness without running the full training cycle, a fast dev run on a single batch is available using the following command:

python RNN-Trans_S-MT.py --dev_run

Citation

If you would like to use our work and code for research, please cite our paper with the following info:

@inproceedings{zhang-etal-2021-none-severe,
    title = "From None to Severe: {P}redicting Severity in Movie Scripts",
    author = "Zhang, Yigeng  and
      Shafaei, Mahsa  and
      Gonzalez, Fabio  and
      Solorio, Thamar",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.332",
    pages = "3951--3956",
}

Contact

Please contact yzhang168@uh.edu for questions.

RiTUAL-UH/Predicting-Severity-in-Movie-Scripts