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TensorFlow implementation of the paper `Adversarial Multi-task Learning for Text Classification`

Adversarial Multi-task Learning for Text Classification

TensorFlow implementation of the paper Adversarial Multi-task Learning for Text Classification.

  • The code uses CNN instead of LSTM.
  • The Gradient Reversal Layer is copied from https://github.com/pumpikano/tf-dann.
  • The Orthogonality Constraints loss (diff loss) is copied from 'research/domain_adaptation' of https://github.com/tensorflow/models. The correlation matrix is normalized. Otherwise, the loss value will be too large.

performace using 50d word embedding:

models avg error
mtl 13.75
mtl + adv 12.79
mtl + adv + diff 12.70

To train the model:

cd data/
tar zxvf fudan-mtl-dataset.tar.gz
cd ../
python3 src/main.py --word_dim=50 --build_data
python3 src/main.py --word_dim=50 --adv
python3 src/main.py --word_dim=50 --adv --test