Theano implementation of Deep LSTM Reader & Attentive Reader from Google DeepMind's paper Teaching Machines to Read and Comprehend - Hermann et al. (2015).
I am using processed RC datasets from this repository. The original datasets can be downloaded from https://github.com/deepmind/rc-data or http://cs.nyu.edu/~kcho/DMQA/. Processed ones are just simply concatenation of all data instances and keeping document, question and answer only.
Note: story & question are alias for document & query respectively.
train.py provides an easy interface to train deep/attentive reader,
model/*_reader.py contains the actual code for model definition and training. Please note that call to
use_existing_model=True will replace the current existing best model with the new best model, so save your intermediate models accordingly.
eval.py provide interface to compute various performance params (accuracy, f1-score) for trained models.
ask.py to let the model infer from your stories and questions.
This code uses portion of Data reading interface written by Danqi Chen.