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Information and Instructions about the code

  • To run the code, Python 3.6 and PyTorch 0.2 are needed
  • The training code is present in the file mtl_learning.py whereas the testing code is present in the file mtl_testing.py
  • The code in mtl_learning.py is pretty much self documented
  • For the training, the datasets should be present as pickle files in the pickles/ directory. The embeddings file should be present in the data/ directory.
  • In case of reloading the models and continuing the training, the models should be placed in reloads/ directory.
  • When all the necessary files and data are present, simply run python mtl_learning.py for training and python mtl_testing.py for testing.
  • The outputs of the training would be present in a file named outputs.txt and that of testing would be present in test_output.txt

Credits

The project is inspired from Pasunuru et al's (2017) work on "Towards Improving Abstractive Summarization via Entailment Generation" and Sean Robertson's tutorial on seq2seq translation.