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Natural Language Inference Enhanced by Knowledge Graph Embedding (KEIM)

Dependencies

To run it perfectly, you will need (recommend using Anaconda to set up environment):

  • Python 3.5 or 3.6
  • Tensorflow 1.10.0
  • Java >= 1.6

1. Download and preprocess

cd data_process

Download the data and the resources for preprocess:

  • SNLI dataset
  • MultiNLI dataset
  • SciTail dataset
  • GloVe embedding (300D)
  • Wordnet 3.0
  • CoreNLP
python download.py

Preprocess for SNLI dataset

python preprocess_data_snli.py

Preprocess for MultiNLI dataset

python preprocess_data_multinli.py

Preprocess for SciTail dataset

python process_data_scitail.py

2. Train KEIM

Hyper-parameters are set in configure file in ./config/xxx.sample.config

Training process for SNLI dataset

cd src
python Main.py --config_path ../configs/snli.sample.config

Training process for MultiNLI dataset

python Main.py --config_path ../configs/multinli.sample.config

Training process for SciTail dataset

python Main.py --config_path ../configs/scitail.sample.config

The model and results are saved in $model_dir$.

3. Evaluation

cd src
python Evaluation.py --model_prefix your_model --in_path The path to the test file.