- Resources for our WSDM 2022 paper: "[Diversified Query Generation Guided by Knowledge Graph]"
- Paper link: https://dl.acm.org/doi/abs/10.1145/3488560.3498431
- CUDA > 11
- Prepare requirements:
pip3 install -r requirements.txt
.
-
python make_dataset.py -config config/demo/demo-prep.yml
: convert raw corpus to dealable datasets. -
python preprocess.py -config config/demo/demo-prep.yml
: opennmt style preprocess. -
python train.py -config config/demo/demo-train.yml
-
python translate.py -config config/demo/demo-transl.yml -model exp/demo/models/DEMO_step_0.pt
- Use
my_feature_extractor.py
andccig.py
to build graph. - We can do train and inference with
train.py
. - For evaluation, Use multi-bleu.perl.