This is the Pytorch implementation of HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition in NAACL-2019.

Please find the datasets via the following links:

1. Friends
2. EmotionPush
3. IEMOCAP

For each dataset, we need to preprocess it using the Preprocess.py file as:

python Preprocess.py -emoset Friends -min_count 2 -max_length 60

The arguments -emoset, -min_count, and -max_length represent the dataset name, the minimum frequency of words when building the vocabulary, and the max_length for padding or truncating sentences.

You can run the exec_emo.sh file in you Bash as:

bash exec_emo.sh

Or you can set up the model parameters yourself:

python EmoMain.py \
-lr 2e-4 \
-gpu 0 \
-type higru-sf \
-d_h1 300 \
-d_h2 300 \
-report_loss 720 \
-data_path Friends_data.pt \
-vocab_path Friends_vocab.pt \
-emodict_path Friends_emodict.pt \
-tr_emodict_path Friends_tr_emodict.pt \
-dataset Friends \
-embedding Friends_embedding.pt

The implementation supports both CPU and GPU (but only one GPU), you need to specify the device number of GPU in your arguments otherwise the model will be trained in CPU. There are three modes in this implementation, i.e., higru, higru-f, and higru-sf, as described in the paper. You can select one of them by the argument -type. The default sizes of the hidden states in the GRUs are 300, but smaller values also work well (larger ones may result in over-fitting). You have to load in the data produced by the Preprocess.py file, e.g., including Friends_data.pt, Friends_vocab.pt, Friends_emodict.pt, and Friends_tr_emodict.pt, as well as the dataset name Friends. The argument -embedding is optional that you can load in the embeddings saved by the first run or the implementation will initialize it every time (which is time-consuming). There are some other arguments in the EmoMain.py file, e.g., the decay rate for learning rate, the patience for early stopping. You can find out and change them if necessary.

Please kindly cite our paper:

@article{jiao2019higru,
  title={HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition},
  author={Jiao, Wenxiang and Yang, Haiqin and King, Irwin and Lyu, Michael R},
  journal={arXiv preprint arXiv:1904.04446},
  year={2019}
}