Exploiting BERT End-to-End Aspect-Based Sentiment Analysis

• python 3.7.3
• pytorch 1.2.0
• transformers 2.0.0
• numpy 1.16.4
• tensorboardX 1.9
• tqdm 4.32.1
• some codes are borrowed from allennlp (https://github.com/allenai/allennlp, an awesome open-source NLP toolkit) and transformers (https://github.com/huggingface/transformers, formerly known as pytorch-pretrained-bert or pytorch-transformers)

• Pre-trained embedding layer: BERT-Base-Uncased (12-layer, 768-hidden, 12-heads, 110M parameters)
• Task-specific layer:
  • Linear
  • Recurrent Neural Networks (GRU)
  • Self-Attention Networks (SAN, TFM)
  • Conditional Random Fields (CRF)

• Restaurant: retaurant reviews from SemEval 2014 (task 4), SemEval 2015 (task 12) and SemEval 2016 (task 5)
• Laptop: laptop reviews from SemEval 2014

• Reproduce the results on Restaurant and Laptop dataset:

  # train the model with 5 different seed numbers
  python fast_run.py 
  

• Train the model on other ABSA dataset:

  1. place data files in the directory ./data/[YOUR_DATASET_NAME].

  2. set TASK_NAME in train.sh as [YOUR_DATASET_NAME].

  3. train the model: sh train.sh

• OS: REHL Server 6.4 (Santiago)
• GPU: NVIDIA GTX 1080 ti
• CUDA: 10.0
• cuDNN: v7.6.1

If the code is used in your research, please star our repo and cite our paper as follows:

@article{li2019exploiting,
  title={Exploiting BERT for End-to-End Aspect-based Sentiment Analysis},
  author={Li, Xin and Bing, Lidong and Zhang, Wenxuan and Lam, Wai},
  journal={arXiv preprint arXiv:1910.00883},
  year={2019}
}