uhhyunjoo / CoMPM

Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation (NAACL 2022)

CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation (NAACL 2022)

The overall flow of our model

Requirements

1. Pytorch 1.8
2. Python 3.6
3. Transformer 4.4.0
4. sklearn

Datasets

Each data is split into train/dev/test in the dataset folder.

1. IEMOCAP
2. DailyDialog
3. MELD
4. EmoryNLP

Train

For CoMPM, CoMPM(s), CoMPM(f)

Argument

• pretrained: type of model (CoM and PM) (default: roberta-large)
• initial: initial weights of the model (pretrained or scratch) (default: pretrained)
• cls: label class (emotion or sentiment) (default: emotion)
• dataset: one of 4 dataset (dailydialog, EMORY, iemocap, MELD)
• sample: ratio of the number of the train dataset (default: 1.0)
• freeze: Whether to learn the PM or not

python3 train.py --initial {pretrained or scratch} --cls {emotion or sentiment} --dataset {dataset} {--freeze}

For a combination of CoM and PM (based on different model)

Argument

• context_type: type of CoM
• speaker_type: type of PM

cd CoMPM_diff
python3 train.py {--argument}

For CoM or PM

cd CoM or PM
python3 train.py {--argument}

Testing with pretrained CoMPM

• Google drive
• Unpack model.tar.gz and replace it in {dataset}_models/roberta-large/pretrained/no_freeze/{class}/{sampling}/model.bin
  • dataset: dailydialog, EMORY, iemocap, MELD
  • class: "emotion" or "sentiment"
  • sampling: 0.0 ~ 1.0, default: 1.0

python3 test.py

Test result for one seed. In the paper, the performance of CoMPM was reported as an average of three seeds.

Model	Dataset (emotion)	Performace: one seed (paper)
CoMPM	IEMOCAP	66.33 (66.33)
CoMPM	DailyDialog	52.46/60.41 (53.15/60.34)
CoMPM	MELD	65.53 (66.52)
CoMPM	EmoryNLP	38.56 (37.37)

Citation

@article{lee2021compm,
  title={CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation},
  author={Lee, Joosung and Lee, Wooin},
  journal={arXiv preprint arXiv:2108.11626},
  year={2021}
}