Implementation of the paper Attention-over-Attention Neural Networks for Reading Comprehension in tensorflow
Some context on my blog
Reading comprehension for cloze style tasks is to remove word from an article summary, then read the article and try to infer the missing word. This example works on the CNN news dataset.
With the same hyperparameters as reported in the paper, this implementation got an accuracy of 74.3% on both the validation and test set, compared with 73.1% and 74.4% reported by the author.
To train a new model: python model.py --training=True --name=my_model
To test accuracy: python model.py --training=False --name=my_model --epochs=1 --dropout_keep_prob=1
Note that the tfrecords and model files are stored with git lfs
Raw data for use with reader.py to produce .tfrecords files was downloaded from [http://cs.nyu.edu/~kcho/DMQA/]
Interesting parts
- Masked softmax implementation
- Example of batched sparse tensors with correct mask handling
- Example of pointer style attention
- Test/validation split part of the tf-graph