This is the implementation of Enhancing Aspect-level Sentiment Analysis with Word Dependencies at EACL 2021.
You can e-mail Yuanhe Tian at yhtian@uw.edu
if you have any questions.
If you use or extend our work, please cite our paper at EACL 2021.
@inproceedings{tian-etal-2021-enhancing,
title = "Enhancing Aspect-level Sentiment Analysis with Word Dependencies",
author = "Tian, Yuanhe and Chen, Guimin and Song, Yan",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
year = "2021",
}
Our code works with the following environment.
python=3.7
pytorch=1.3
To obtain the data, you can go to data
directory for details.
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.
For ASA-WD, you can download the models we trained in our experiments from Google Drive or Baidu Net Disk (passwword: ga1w).
Run run_sample.sh
to train a model on the small sample data under the sample_data
directory.
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.
- Release the code to get the data.
- Regular maintenance.
You can leave comments in the Issues
section, if you want us to implement any functions.