A Implementation with Dilation Gate CNN For Machine Reading Comprehension.

Requirements

• python==3.x (Let's move on to python 3 if you still use python 2)
• tensorflow>=1.12.0
• tqdm>=4.28.1

Model Structure

• This model is come from JianLin Su. This is this model blog from him. Thanks for him of give him idea public, and I add bert to this model, just use pretrain bert vector, use bert word vector to replace the word2vec, so the vocab is from bert vocab, After I add bert to this model, the GPU memory spending is so large, if u want to train this model, to be sure you have large model training environment.
• I also implement other embedding getting way, It's word2vec, you can find in another branch.

Structure

<img src="fig/structure.png">

Training

You can use WebQA to train this model, or you want to change the dataset to yours, change the way of load data in data_load.py

• Run

python train.py --logdir myLog --batch_size 32 --train myTrain --eval myEval --bert_pre bertPreTrain