This repo contains the pytorch implementation of Label-aware Contrastive Loss (LCL).

Download datasets from the given data source links, Empathetic Dialogue, GoEmotions, ISEAR, EmoInt SST-2,SST-5

For Empathetic Dialogue dataset, an additional extraction of the raw data was done to get the csv files using ed_data_extract.py which is required for the below pre-processing

python data_preprocess.py -d <dataset name> --aug ## for emotion datasets
python sst_data_preprocess.py -d <dataset name> --aug ## for sentiment datasets

Set the parameters in config.py

python train.py

We took help from the following open source projects and we acknowledge their contributions.

1. Supervised Contrastive Loss https://github.com/HobbitLong/SupContrast
2. SST-tree2tabular https://github.com/prrao87/fine-grained-sentiment/blob/master/data/sst/tree2tabular.py
3. Tweet preprocess https://github.com/cbaziotis/ntua-slp-semeval2018.git
4. Tweet preprocess https://github.com/abdulfatir/twitter-sentiment-analysis.git

Note: There is minor typo in Eq 3 in the paper relating to the placement of weight wi yi (i.e the positive weights which are constant for a given sample). It is meant to be outside the log and is correctly implemented in the code. The results in the paper follow the current implementation.