The overall flow of our model

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

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

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

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

In this code, the batch size = 1. We do not add padding when the batch is greater than 1.

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}

• 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.

@inproceedings{lee-lee-2022-compm,
    title = "{C}o{MPM}: Context Modeling with Speaker{'}s Pre-trained Memory Tracking for Emotion Recognition in Conversation",
    author = "Lee, Joosung  and
      Lee, Wooin",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.416",
    doi = "10.18653/v1/2022.naacl-main.416",
    pages = "5669--5679",
}