ASA-TGCN

This is the implementation of Aspect-based Sentiment Analysis withType-aware Graph Convolutional Networks and Layer Ensemble at NAACL 2021.

You can e-mail Guimin Chen at chenguimin@foxmail.com or cuhksz.nlp@gmail.com or Yuanhe Tian at yhtian@uw.edu, if you have any questions.

Citation

If you use or extend our work, please cite our paper at NAACL 2021.

@inproceedings{tian-etal-2021-aspect,
    title = "Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble",
    author = "Tian, Yuanhe  and
      Chen, Guimin  and
      Song, Yan",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    pages = "2910--2922"
}

Requirements

Our code works with the following environment.

python=3.7
pytorch=1.3

Dataset

To obtain the data, you can go to data directory for details.

Downloading BERT and ASA-TGCN

In our paper, we use BERT (paper) as the encoder.

For BERT, please download pre-trained BERT-Base and BERT-Large English from Google or from HuggingFace. If you download it from Google, you need to convert the model from TensorFlow version to PyTorch version.

Run on Sample Data

Run run_sample.sh to train a model on the small sample data under the sample_data directory.

Training and Testing

You can find the command lines to train and test models in run_train.sh and run_test.sh, respectively.

Here are some important parameters:

--do_train: train the model.
--do_eval: test the model.

To-do List

Release the models.
Regular maintenance.

You can leave comments in the Issues section, if you want us to implement any functions.

JingliSHI0206/ASA-TGCN