In order to extract the features from the corpus proto files, run:

python data_processing/data_generation.py

In order for the command to be successful, it is necessary to have a directory corpus/r252-corpus-features with the protos of the corpus. Optionally, it is possible to downloaded the extracted dataset at https://drive.google.com/file/d/14k4AgOVws4_TfPtDGefXzPn3x2Ph083h/view?usp=sharing. After putting the downloaded file under the data/ directory (which needs to be created), it is possible to train and evaluate the model.

In order to train a model and evaluate a model, run:

python training/train.py --model_name="lstm_gcn_to_lstm_attention" --print_every=10000 --attention=True --graph=True --iterations=500000

All the possible options when running a model can be seen by running:

python train.py --help

A pretrained version of the best performing model (as a state dictionary) can be downloaded at https://drive.google.com/file/d/1fm7hGzr-tziNhUMh8duc8s4j5gWW3uKm/view?usp=sharing

• data_processing/: contains the code for extracting, storing, analysing and processing data
  • data_analysis.ipynb: notebook containing analysis of the extracted data
  • data_extraction.py: contains the logic to extract the features data from the proto files of the corpus
  • data_generation.py: file to be called to generate the features data
  • data_util.py: contains utilities to work with data
  • text_util.py: contains utilities to work with text
• models/: contains all the code for the different models
  • full_model.py: class of the complete methodNaming model
  • gat_encoder.py: class for the Graph Attention Network encoder
  • gcn_encoder.py: class for the Graph Convolutional Network encoder
  • graph_attention_layer.py: class for the Graph Attention Layer used by the Graph Attention Network
  • graph_convolutional_layer.py: class for the Graph Convolutional Layer used by the Graph Convolutional Network
  • lstm_decoder.py: class for the LSTM sequence decoder
  • lstm_encoder.py: class for the LSTM sequence encoder
• training.py: contains code to train and evaluate the models
  • evaluation_util.py: contains utilities to compute evaluation metrics
  • train.py: entry-point for training the models
  • train_model.py: contains logic to train the models