Recently, emotion recognition in conversation (ERC) has become more crucial in the development of diverse Internet of Things devices, especially closely connected with users. The majority of deep learning-based methods for ERC combine the multilayer, bidirectional, recurrent feature extractor and the attention module to extract sequential features. In addition to this, the latest model utilizes speaker information and the relationship between utterances through the graph network. However, before the input is fed into the bidirectional recurrent module, detailed intrautterance features should be obtained without variation of characteristics. In this article, we propose a residual-based graph convolution network (RGCN) and a new loss function. Our RGCN contains the residual network (ResNet)-based, intrautterance feature extractor and the GCN-based, interutterance feature extractor to fully exploit the intra–inter informative features. In the intrautterance feature extractor based on ResNet, the elaborate context feature for each independent utterance can be produced. Then, the condensed feature can be obtained through an additional GCN-based, interutterance feature extractor with the neighboring associated features for a conversation. The proposed loss function reflects the edge weight to improve effectiveness. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.

• Anaconda3

• Python == 3.6

  conda create --name rgcn python=3.6

• PyTorch (NVIDIA GPU CUDA)

  Trained on PyTorch 1.0.0 CUDA 10.0

  conda install pytorch==1.0.0 torchvision==0.2.1 cuda100 -c pytorch
  conda install cudatoolkit=10.0 -c pytorch

• torch-geometric, torch-sparse, torch-scatter, torch-cluster

  pip install torch-geometric==1.1.0
  pip install torch-sparse==0.2.4
  pip install torch-scatter==1.1.2
  pip install torch-cluster==1.2.4

• pickle, pandas, scikit-learn, spacy, torchtext

  pip install pickle-mixin
  pip install pandas
  pip install scikit-learn
  pip install spacy
  pip install torchtext

We used the interactive emotional dyadic motion capture (IEMOCAP), MELD, EmoContext (EC) datasets.

[IEMOCAP] Busso, Carlos, et al. "IEMOCAP: Interactive emotional dyadic motion capture database." Language resources and evaluation 42.4 (2008): 335-359.
[MELD] Poria, Soujanya, et al. "Meld: A multimodal multi-party dataset for emotion recognition in conversations." arXiv preprint arXiv:1810.02508 (2018).
[EC] Chatterjee, Ankush, et al. "Semeval-2019 task 3: Emocontext contextual emotion detection in text." Proceedings of the 13th international workshop on semantic evaluation. 2019.

• Download

  You can download three datasets in below link.

  

  Put the datasets in ./datasets/

Pre-trained models are available in below link.

Also, we used the '300-dimensional pretrained 840B GloVe' (glove.840B.300d) for embedding and you can download the glove.840B.300d in here.

Run in ./codes/

• IEMOCAP

  python train_RGCN_IEMOCAP.py

• MELD

  python train_RGCN_MELD.py

• EC

  python train_RGCN_EC.py

Run in ./codes/

• IEMOCAP

  python predict_RGCN_IEMOCAP.py

• MELD

  python predict_RGCN_MELD.py

• EC

  python predict_RGCN_EC.py

@article{choi2021residual,
    title={Residual-based graph convolutional network for emotion recognition in conversation for smart Internet of Things},
    author={Choi, Young-Ju and Lee, Young-Woon and Kim, Byung-Gyu},
    journal={Big Data},
    volume={9},
    number={4},
    pages={279--288},
    year={2021},
    publisher={Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…}
}

The codes are heavily based on DialogueGCN. Thanks for their awesome works.

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation. D. Ghosal, N. Majumder, S. Poria, N. Chhaya, & A. Gelbukh. EMNLP-IJCNLP (2019), Hong Kong, China.