HatReD: Decoding the Underlying Meaning of Multimodal Hateful Memes

A public repository containing Hateful memes Reasoning Dataset (HatReD) and code implementation for the paper "Decoding the Underlying Meaning of Multimodal Hateful Memes" (IJCAI'23)

Table of Contents

Dataset

The HatReD dataset contains the social target(s) and reasoning(s) annotations for hateful memes, which can be found in the datasets/hatred/annotations folder

Baseline Experiments

Build HatReD's train and test dataset

To reproduce the models used in the experiments, you will need to create the training and testing dataset for HatReD using the original Facebook AI's Hateful Memes dataset. To do so, you can refer to the README file located in the datasets` section.

Encoder Decoder Models

Installation

Step 1. Navigate to encoder decoder model subdirectory

cd encoder-decoder-models

Step 2. You can find the necessary packages under requirements.txt. You can install the packages using the following command:

conda create -n ed-models python=3.8 -y
conda activate ed-models
pip install --upgrade pip  # enable PEP 660 support
pip install -r requirements

Training

You can use main.py to execute the experiment using different encoder decoder models (i.e. T5, RoBERTa-RoBERTa). For your convenience, we have prepared the respective training scripts for each model settings is provided under encoder-decoder-models/scripts/train folder.

These scripts can also serve as reference point on how you can use the main.py file. For instance, you can train the T5 model on HatReD dataset using the following command:

bash scripts/train/t5.sh

Testing

Similarly, you can find the respective evaluation scripts under scripts/test folder. You can evaluate the trained T5 model on HatReD dataset using the following command:

bash scripts/test/t5.sh

VL-T5

Installation

Step 1. Navigate to VL-T5 subdirectory

cd VL-T5

Step 2. You can find the necessary packages under requirements.txt. You can install the packages using the following command:

conda create -n vl-t5 python=3.8 -y
conda activate vl-t5
pip install --upgrade pip  # enable PEP 660 support
pip install -r requirements

Step 3. Download the pretrained model (provided by the VL-T5 authors)

Training

You can use src/fhm.py to execute the experiment. For your convenience, we have prepared the training scripts under VL-T5/VL-T5/scripts/train folder.

The script will also serve as reference point on how you can use the src/fhm.py file. For instance, you can train the VL-T5 on HatReD dataset using the following command:

cd VL-T5
bash scripts/train/FHM_VLT5.sh

Testing

Similarly, you can find the evaluation script under VL-T5/VL-T5/scripts/test folder. You can evaluate the trained VL-T5 model on HatReD dataset using the following command:

bash scripts/test/FHM_VLT5.sh

Citations

If you find HatReD useful for your your research and applications, please cite the following works using this BibTeX:

@inproceedings{hee2023hatred,
  title={Decoding the Underlying Meaning of Multimodal Hateful Memes},
  author={Hee, Ming Shan and Chong, Wen-Haw and Lee, Ka-Wei Roy},
  booktitle={32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)},
  year={2023},
  organization={International Joint Conferences on Artifical Intelligence (IJCAI)}
}

Additionally, you should also cite the following datasets

@article{kiela2020hateful,
  title={The hateful memes challenge: Detecting hate speech in multimodal memes},
  author={Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Ringshia, Pratik and Testuggine, Davide},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={2611--2624},
  year={2020}
}

@inproceedings{fersini2022semeval,
  title={SemEval-2022 Task 5: Multimedia automatic misogyny identification},
  author={Fersini, Elisabetta and Gasparini, Francesca and Rizzi, Giulia and Saibene, Aurora and Chulvi, Berta and Rosso, Paolo and Lees, Alyssa and Sorensen, Jeffrey},
  booktitle={Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)},
  pages={533--549},
  year={2022}
}