• A CNTK(Microsoft deep learning toolkit) implementation of S-NET: FROM ANSWER EXTRACTION TO ANSWER GENERATION FOR MACHINE READING COMPREHENSION with some modifications.
• This project is designed for the MSMARCO dataset
• Code structure is based on CNTK BIDAF Example
• Support MSMARCO V1 and V2!

Here are some required libraries for training.

• python3.6
• cuda-9.0 (CNTK required)
• openmpi-1.10 (CNTK required)
• gcc >= 6 (CNTK required)

• Please refer requirements.txt

Download MSMARCO v1 dataset, GloVe embedding.

cd data
python3.6 download.py v1

Convert raw data to tsv format

python3.6 convert_msmarco.py --threads=`nproc` 

Convert tsv format to ctf(CNTK input) format and build vocabs dictionary

python3.6 tsv2ctf.py

Generate elmo embedding

sh elmo.sh

Download MSMARCO v2 dataset, GloVe embedding.

cd data
python3.6 download.py v2

Convert raw data to tsv format

python3.6 convert_msmarco.py --threads=`nproc` --ratio=0.1

Convert tsv format to ctf(CNTK input) format and build vocabs dictionary

python3.6 tsv2ctf.py

Generate elmo embedding

sh elmo.sh

cd ../script
mkdir log
sh run.sh

cd Evaluation
sh eval.sh v1

cd Evaluation
sh eval.sh v2

• Multi-threads preprocessing
• Elmo-Embedding
• Evaluation script
• MSMARCO v2 support
• Reasonable metrics