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CoMPM

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

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# CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation (NAACL 2022)
![model](https://raw.githubusercontent.com/rungjoo/CoMPM/master/image/model.png)
The overall flow of our model

## Requirements
1. Pytorch 1.8
2. Python 3.6
3. [Transformer 4.4.0](https://github.com/huggingface/transformers)
4. sklearn

## Datasets
Each data is split into train/dev/test in the [dataset folder](https://github.com/rungjoo/CoMPM/tree/master/dataset).
1. [IEMOCAP](https://sail.usc.edu/iemocap/iemocap_publication.htm)
2. [DailyDialog](http://yanran.li/dailydialog.html)
3. [MELD](https://github.com/declare-lab/MELD/)
4. [EmoryNLP](https://github.com/emorynlp/emotion-detection)

## Train
**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.](https://github.com/rungjoo/CoMPM/issues/2)

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

```bash
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
```bash
cd CoMPM_diff
python3 train.py {--argument}
```

**For CoM or PM**
```bash
cd CoM or PM
python3 train.py {--argument}
```

## Testing with pretrained CoMPM
- [Naver drive](http://naver.me/FFi8Y74W)
- 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
    
```bash
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

```bibtex
@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",
}
```