alaaalatif / neuralattention_for_rte

This is an attempted implementation of the paper titled "Reasoning about Entailment using Neural Attention". This package will likely include the data sets needed to carry out validation tests, but incase it is not: Download the SNLI data sets from: http://nlp.stanford.edu/projects/snli/ Unzip the folder and move it inside this folder $cp -r [PATH_TO_SNLI}/snli_1.0 .

Carry out data preprocessing using $python data_preprocess.py

This requires the following Python modules to be installed: gensim nltk pandas pickle numpy

Then, carry out training and testing using $THEANO_FLAGS='mode=FAST_RUN,floatX=float32,device=cpu' python neuralAttention.py word_by_word

This requires the bleeding edge versions of Theano and lasagne, which can be installed using: $pip install --upgrade https://github.com/Theano/Theano/archive/master.zip $pip install --upgrade https://github.com/Lasagne/Lasagne/archive/master.zip

The main layers of the RNN are present in the layer.py file. They are essentially derivative classes from lasagne.layers.Layer and lasagne.layers.LSTMLayer

At the time of writing this, I have not yet ran the full experiment and thus do not know the accuracy that is achieved (or honestly if it doesn't give an error)