DAIE

This is the official implementation of the paper "Dual-Level Adaptive Incongruity-Enhanced Model for Multimodal Sarcasm Detection", which is accepted by Neurocomputing. (https://doi.org/10.1016/j.neucom.2024.128689)

Model Architecture

The framework of dual-level adaptive incongruity-enhanced model (DAIE).

Environment Requirements

The experiments were conducted on a single GeForce RTX 3090 GPU with 24GB memory.

• Python 3.7.2
• PyTorch 1.8.0+cu111
• CUDA 11.2

To run the code, you need to install the requirements:

pip install -r requirements.txt

Data Download

We evaluate our model using a publicly available multimodal sarcasm detection dataset. For the orginial dataset, see as https://github.com/headacheboy/data-of-multimodal-sarcasm-detection.

To run our code and for a fair comparison, we adhere to the preprocessing methods outlined in previous work. Please replace paths of datasets in DATA_PATH and IMG_PATH of main.py using your paths.

Run Code

At last, you can run the below code:

bash run.sh

Papers for the Project & How to Cite

If you use or extend our work, please cite the paper as follows:

@article{wu2024dual,
  title={Dual-level adaptive incongruity-enhanced model for multimodal sarcasm detection},
  author={Wu, Qiaofeng and Fang, Wenlong and Zhong, Weiyu and Li, Fenghuan and Xue, Yun and Chen, Bo},
  journal={Neurocomputing},
  pages={128689},
  year={2024},
  publisher={Elsevier}
}