It is a question-generator model. It takes text and an answer as input and outputs a question.

Question generator model trained in seq2seq setup by using http://opennmt.net.

• Docker ver. 17.03+:

  • Ubuntu: https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository
  • Mac: https://download.docker.com/mac/stable/Docker.dmg

• Docker-compose ver. 1.13.0+: https://docs.docker.com/compose/install/

• Python 3

• pyzmq dependencies: Ubuntu sudo apt-get install libzmq3-dev or for Mac brew install zeromq --with-libpgm

• run ./setup. This script downloads torch question generation model, installs python requirements, pulls docker images and runs opennmt and corenlp servers.

./get_qnas "<text>" - takes as input text and outputs tsv.

• First column is a question,
• second column is an answer,
• third column is a score.

Input:

./get_qnas "Waiting had its world premiere at the \
  Dubai International Film Festival on 11 December 2015 to positive reviews \
  from critics. It was also screened at the closing gala of the London Asian \
  Film Festival, where Menon won the Best Director Award."

Output:

who won the best director award ? menon -2.38472032547
when was the location premiere ?  11 december 2015  -6.1178450584412

• First model feeding may take a long time because of CoreNLP modules loading.
• Do not forget to install pyzmq dependencies.