DLCF-DCA

codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted to 《Neurocomputing》.

Requirement

• Python >= 3.6
  
• PyTorch >= 1.0
  
• pytorch-transformers == 1.2.0
  
• SpaCy >= 2.2

To use our models, you need download en_core_web_sm by python -m spacy download en_core_web_sm

Training

python train.py --model dlcf_dca

Model Architecture

Note

Some important scripts to note:

• datasets/semeval14/*.seg: Preprocessed training and testing sentences in SemEval2014.
• datasets/semeval15/*.seg: Preprocessed training and testing sentences in SemEval2015.
• datasets/semeval16/*.seg: Preprocessed training and testing sentences in SemEval2016.
• models/dlcf_dca.py: the source code of DLCF_DCA model.
• data_utils.py/ABSADataSet class: preprocess the tokens and calculates the shortest distance to target words and cluster via the Dependency Syntax Parsing Tree.

Out of Memory

Since BERT models require a lot of memory. If the out-of-memory problem while training the model, here are the ways to mitigate the problem:

1. Reduce the training batch size ( batch_size = 4 or 8 )
2. Reduce the longest input sequence ( max_seq_len = 40 or 60 )
3. Set use_single_bert = true to use a unique BERT layer to model for both local and global contexts

Acknowledgement

We have based our model development on https://github.com/songyouwei/ABSA-PyTorch. Thanks for their contribution.