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README.md

ActiveQA: Active Question Answering

This repo contains code for our paper Ask the Right Questions: Active Question Reformulation with Reinforcement Learning.

Small forewarning, this is still much more of a research codebase than a library. No support is provided.

If you use this code for your research, please cite the paper.

Introduction

ActiveQA is an agent that transforms questions online in order to find the best answers. The agent consists of a Tensorflow model that reformulates questions and an Answer Selection model. It interacts with an environment that contains a question-answering system. The agent queries the environment with variants of a question and calculates a score for the answer against the original question. The model is trained end-to-end using reinforcement learning.

This version addresses the SearchQA question-answering task, and the environment consists of the Bi-directional Attention Flow (BiDAF) model of Seo et al. (2017).

Setup

Dependencies

We require tensorflow and many other supporting libraries. Tensorflow should be installed separately following the docs. To install the other dependencies use

pip install -r requirements.txt

Note: We only ran this code with Python 2, so Python 3 is not officially supported.

Data

Download the source dataset from SearchQA, GloVe, and NLTK corpus and save them in $HOME/data.

export DATA_DIR=$HOME/data
mkdir $DATA_DIR

Download

Download the SearchQA dataset (~600 MB) for training, testing, and validation here: https://drive.google.com/open?id=1OxRhw81g7amW3aBd_iu2By5THysgr2uv

<Download the dataset to $DATA_DIR/SearchQA.zip>
unzip $DATA_DIR/SearchQA.zip -d $DATA_DIR

Download GloVe (~850 MB):

export GLOVE_DIR=$DATA_DIR/glove
mkdir $GLOVE_DIR

wget -c http://nlp.stanford.edu/data/glove.6B.zip -O $GLOVE_DIR/glove.6B.zip
unzip $GLOVE_DIR/glove.6B.zip -d $GLOVE_DIR

python -m nltk.downloader -d $HOME/nltk_data punkt

export PRETRAINED_DIR=$DATA_DIR/pretrained
mkdir $PRETRAINED_DIR

wget -c https://storage.googleapis.com/pretrained_models/translate.ckpt-1460356.zip -O $PRETRAINED_DIR/translate.ckpt-1460356.zip
unzip $PRETRAINED_DIR/translate.ckpt-1460356.zip -d $PRETRAINED_DIR

Convert into SQuAD data format as the model was written to only work with that format.

export SQUAD_DIR=$DATA_DIR/squad
mkdir $SQUAD_DIR

python -m searchqa.prepro \
--searchqa_dir=$DATA_DIR/SearchQA \
--squad_dir=$SQUAD_DIR

Preprocess the SearchQA dataset in SQuAD format (along with GloVe vectors) and save them in $PWD/data/squad (~60 minutes):
```
python -m third_party.bi_att_flow.squad.prepro \
--glove_dir=$GLOVE_DIR \
--source_dir=$SQUAD_DIR
```

chmod +x compile_protos.sh; ./compile_protos.sh

python -m px.environments.bidaf_server \
--port=10000 \
--squad_data_dir=data/squad \
--bidaf_shared_file=data/bidaf/shared.json \
--bidaf_model_dir=data/bidaf/

python basic/cli.py \
--mode=trains \
--data_dir=data/squad \
--shared_path=data/bidaf/shared.json \
--init_lr=0.001 \
--num_steps=14000

export OUT_DIR=/tmp/active-qa
mkdir $OUT_DIR

export REFORMULATOR_DIR=$OUT_DIR/reformulator
mkdir $REFORMULATOR_DIR

echo "model_checkpoint_path: \"$PRETRAINED_DIR/translate.ckpt-1460356\"" > checkpoint
cp -f checkpoint $REFORMULATOR_DIR
cp -f checkpoint $REFORMULATOR_DIR/initial_checkpoint.txt

python -m px.nmt.reformulator_and_selector_training \
--environment_server_address=localhost:10000 \
--hparams_path=px/nmt/example_configs/reformulator.json \
--enable_reformulator_training=true \
--enable_selector_training=false \
--train_questions=$SQUAD_DIR/train-questions.txt \
--train_annotations=$SQUAD_DIR/train-annotation.txt \
--train_data=data/squad/data_train.json \
--dev_questions=$SQUAD_DIR/dev-questions.txt \
--dev_annotations=$SQUAD_DIR/dev-annotation.txt \
--dev_data=data/squad/data_dev.json \
--glove_path=$GLOVE_DIR/glove.6B.100d.txt \
--out_dir=$REFORMULATOR_DIR \
--tensorboard_dir=$OUT_DIR/tensorboard

python -m px.nmt.reformulator_and_selector_training \
--environment_server_address=localhost:10000 \
--hparams_path=px/nmt/example_configs/reformulator.json \
--enable_reformulator_training=false \
--enable_selector_training=true \
--train_questions=$SQUAD_DIR/train-questions.txt \
--train_annotations=$SQUAD_DIR/train-annotation.txt \
--train_data=data/squad/data_train.json \
--dev_questions=$SQUAD_DIR/dev-questions.txt \
--dev_annotations=$SQUAD_DIR/dev-annotation.txt \
--dev_data=data/squad/data_dev.json \
--glove_path=$GLOVE_DIR/glove.6B.100d.txt \
--batch_size_train=16 \
--batch_size_eval=64 \
--save_path=$OUT_DIR/selector \
--out_dir=$REFORMULATOR_DIR \
--tensorboard_dir=$OUT_DIR/tensorboard

@inproceedings{buck18,
  author    = {Christian Buck and
               Jannis Bulian and
               Massimiliano Ciaramita and
               Andrea Gesmundo and
               Neil Houlsby and
               Wojciech Gajewski and
               Wei Wang},
  title     = {Ask the Right Questions: Active Question Reformulation with Reinforcement
               Learning},
  booktitle = {Sixth International Conference on Learning Representations (ICLR)},
  year      = {2018},
  month     = {May},
  address   = {Vancouver, Canada},
  url       = {https://openreview.net/forum?id=S1CChZ-CZ},
}