MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences

This repository contains the official implementation of the paper: MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences, published at EMNLP 2022.

Setup

Conda Environemnt

conda env create -f environment.yml
conda activate mmalign
python -m spacy download en_core_web_sm

CMU-MOSI and CMU-MOSEI

Please refer to this repository to get the .pkl files that store the extracted features (by CMU-MMSDK with integrated COVAREP and P2FA) of the two datasets.

MELD dataset

You can download the processed dataset (.pkl) from here. Alternatively, if you'd like to extract the features by yourself, you can download the raw dataset from here. Then you can extract the visual and audio features with ResNet101 (FPS=25) and Wave2Vec2.0. Additionally, you need to manually gather text and extracted feature vectors by their IDs and split them into (train/dev/test).pkl files.

Next, split the processed dataset into complete/incomplete partitions using scripts/split_dataset.py

python split_dataset.py --data_path <path_to_pickle_files> --seed <seed> --group_id <group_id> --complete_ratio <complete_ratio> --split <split>

We provide an example script script/run_split.sh, which automatically generates 5 different partitions for a given dataset under the seed 2020-2024.

Train and Test

cd src
python main.py --dataset <dataset_name> --data_path <path_to_dataset> --group_id <group_to_experiment> --modals <modality_pairs> --save_name <name_prefix>

The best test results are automatically saved under results/<save_name>_<modality_pairs>.tsv

Citation

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

@inproceedings{han2022mmalign,
  title={MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences},
  author={Han, Wei and Chen, Hui and Kan Min-Yen and Poria, Soujanya},
  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year={2022}
}

Contact

Should you have any question, feel free to contact me through [email protected]