Code for EMNLP2022 paper "Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation"

Overview

The main contribution of this work is the supervised prototypical contrastive learning(SPCL) loss, it is easy to understand via the following picture.

Requirements

pip install -r requirements

usage

python train.py
  -h, --help            show this help message and exit
  -te, --test           run test
  -tr, --train          run train
  -ft, --finetune       fine tune the best model
  -cl, --cl             use CL
  -pr, --print_error    print error case
  -mlp, --output_mlp    use an additional mlp layer on the model output
  -fgm, --fgm           use fgm
  -bsz BATCH_SIZE, --batch_size BATCH_SIZE
                        Batch_size per gpu
  -seed SEED, --seed SEED
                        seed
  -psz POOL_SIZE, --pool_size POOL_SIZE
                        Batch_size per gpu
  -ssz SUPPORT_SET_SIZE, --support_set_size SUPPORT_SET_SIZE
                        support size per gpu
  -epochs EPOCHS, --epochs EPOCHS
  -cluster_size AVG_CLUSTER_SIZE, --avg_cluster_size AVG_CLUSTER_SIZE
                        avg_cluster_size
  -lr LR, --lr LR       learning rate
  -ptmlr PTMLR, --ptmlr PTMLR 
                        ptm learning rate
  -tsk TASK_NAME, --task_name TASK_NAME
                        it can be meld, iemocap and emorynlp
  -wp WARM_UP, --warm_up WARM_UP
  -dpt DROPOUT, --dropout DROPOUT
  -temp TEMPERATURE, --temperature TEMPERATURE
  -bert_path BERT_PATH, --bert_path BERT_PATH
  -train_obj TRAIN_OBJ, --train_obj TRAIN_OBJ 
                        it can be spcl, spdcl or ce
  -data_path DATA_PATH, --data_path DATA_PATH
  -temp_path TEMP_PATH, --temp_path TEMP_PATH
  -acc_step ACCUMULATION_STEPS, --accumulation_steps ACCUMULATION_STEPS
  

to train and eval the model,

python train.py -tr -wp 128 -epochs 16 -temp 0.05 -tsk meld|iemocap|emorynlp -psz 256 -ssz 64 -train_obj spcl|spdcl|ce -cl -seed 2333

e.g., to train the model on the meld dataset via spcl,

python train.py -tr -wp 128 -epochs 16 -temp 0.05 -tsk meld -psz 256 -ssz 64 -train_obj spcl -cl -seed 2333

key args

• epochs we use epochs to control the difficulty.
• psz size of representations queue
• ssz size of support set
• train_obj we describe the spcl in our paper, and an explanation of spdcl can be seen below
• temp the temperature used in contrastive loss

We use grid search to select the optim values for these args, but as we described in the Limitations, we introduce too many randomness in our code, the optim values of these hyper-parameters changed with the seed.

what is spdcl?

We describe the spcl loss in our paper, meanwhile we develop the decoupled version of spcl, called spdcl, since the decoupled contrastive loss (https://arxiv.org/pdf/2110.06848.pdf) is not the contribution of this paper, we didn't describe this loss in our paper, and all results in the experiments are produced via spcl.

The spdcl shows similar performance with spcl , if you have limited computing resources, the spdcl may a better choice. See details in spcl_loss.py.

If you have any questions, feel free to create issues or contact me at [email protected] or [email protected].