PyTorch Implementation of Dynamic Coattention Network

Implementation of the paper Dynamic Coattention Network https://arxiv.org/pdf/1611.01604.pdf

Improvement ideas

• Use layer normalization

Parts of the architecture explained in brief

Encoder

Encodes both question and context

Coattention Network

Combines attention of question with context

Highway Maxout Network

Determines possible start and end points

Dynamic Decoding

Determines start and end points

What do the py files do

• config.py contains all the configuration

• baseline.py contains a baseline architecture based on tfidf and cosine distance

• vanillaQA.py contains baseline neural network architecture that might possibly work

• squad.py contains data parser for Squad Dataset

• setup.py - you need to run this after installing requirements to download data for nltk

• networks package has all of the networks in separate class for testing purpose