MultimodalResidualOptimization

The code for "Beyond Additive Fusion: Learning Non-Additive Multimodal Interactions" at Findings of EMNLP 2022.

Setup

This code base heavily relies on python-tools to conduct the machine learning experiments.

git clone [email protected]:twoertwein/MultimodalResidualOptimization.git
cd MultimodalResidualOptimization
poetry update  # installs python-tools and all other dependencies

Usage

For early fusion

# MRO
python train.py --dimension <dimension> --res_det
# baseline (joint)
python train.py --dimension <dimension> --res_det --joint
# only tri-modal branch
python train.py --dimension <dimension> --res_det --joint --tri
# routing
python train.py --dimension <dimension> --routing 
# MRO
python train.py --dimension <dimension> --res_det
# sMRO
python train.py --dimension <dimension> --res_det --stepwise

For tensor fusion

# MRO
python train.py --dimension <dimension> --mult --res_det
# baseline (joint)
python train.py --dimension <dimension> --mult
# only tri-modal branch
python train.py --dimension <dimension> --mult --tri
# routing
python train.py --dimension <dimension> --routing --mult 
# sMRO
python train.py --dimension <dimension> --mult --res_det --stepwise

Where <dimension> can be: uni (unimodal sanity check), bi (bimodal sanity check), mosi (sentiment on MOSI), sentiment (MOSEI), polarity (MOSEI), happiness (MOSEI), Arousal (IEMOCAP), Valence (IEMOCAP), arousal (SEWA), valence (SEWA), constructs (TPOT), or intent (Instagram).

Data

The pre-processed features used for the machine-learning experiments for the sanity checks and the Instagram dataset are part of this git repository. The features for MOSI, MOSEI, and TPOT are available here. If you want the features for IEMOCAP and SEWA, please send us proof that you completed the data-sharing agreements required by those projects.

TPOT

The creation of the Transitions in Parenting of Teens (TPOT) dataset was funded by NIH grant #5R01 HD081362 (awarded to Lisa B. Sheeber and Nicholas B. Allen). When referring to the TPOT dataset, please cite

@article{nelson2021psychobiological,
  title={Psychobiological markers of allostatic load in depressed and nondepressed mothers and their adolescent offspring},
  author={Nelson, Benjamin W and Sheeber, Lisa and Pfeifer, Jennifer and Allen, Nicholas B},
  journal={Journal of Child Psychology and Psychiatry},
  volume={62},
  number={2},
  pages={199--211},
  year={2021},
  publisher={Wiley Online Library}
}

Perception Study

The aggregated scores from the perception study on IEMOCAP are in perception_study_arousal.csv and perception_study_arousal.csv. To synchronize the scores with the model output, please use the fields meta_id, meta_begin, and meta_end.