Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis

Pytorch implementation of paper:

Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis

Content

• Data Preparation
• Environment
• Training
• Citation

Data Preparation

MOSI/MOSEI/CH-SIMS Download: See MMSA

Environment

The basic training environment for the results in the paper is Pytorch 1.11.0, Python 3.7 with RTX 3090. It should be noted that different hardware and software environments can cause the results to fluctuate.

Training

You can quickly run the code with the following command (you can refer to opts.py to modify more hyperparameters):

MOSI

python train.py --CUDA_VISIBLE_DEVICES 0 --project_name ALMT_DEMO --datasetName mosi --dataPath ./datasets/unaligned_50.pkl --fusion_layer_depth 2 --is_test 1

Citation

• Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis

Please cite our paper if you find our work useful for your research:

@inproceedings{zhang-etal-2023-learning-language,
    title = "Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis",
    author = "Zhang, Haoyu  and
      Wang, Yu  and
      Yin, Guanghao  and
      Liu, Kejun  and
      Liu, Yuanyuan  and
      Yu, Tianshu",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    year = "2023",
    publisher = "Association for Computational Linguistics",
    pages = "756--767"
}