RFBFN: A Relation-First Blank Filling Network for Joint Entity and Relation Extraction

PyTorch code for RFBFN: A Relation-First Blank Filling Network for Joint Entity and Relation Extraction. For the description of the model and experiments, please see our paper. The model structure is as follows:

Requirements

Python: 3.5+
PyTorch: 1.7.0
transformers: 4.6.1
allennlp: 0.9.0
numpy: 1.19.2
tqdm: 4.60.0

Datasets

• NYT* and WebNLG* ( following CasRel )
• NYT ( following CopyRE )
• WebNLG ( following ETL-span )

We provide preprocessed datasets in ./data_preprocess/data/, you can just download them.

Usage

1. Build Data

   For WebNLG*:

   cd data_preprocess
   python preprocess.py  
   

   For NYT*:

   cd data_preprocess
   python preprocess.py --task "nyt" --duplicate_questions 6
   

2. Train

   After preparing the data for RFBFN model, you can train and test the model.

   For WebNLG*:

   cd ..
   python RFBFN_main.py --gpu_setting "0" --RE_loss_for_RD_parameter 10 --learning_rate_in_RD 4e-5
   

   For NYT*:

   cd ..
   python RFBFN_main.py --task "nyt" --num_decoder_layers_for_RD 3 --queries_num_for_RD 15 --no_rel_reweighting_in_RD 0.6 --learning_rate_for_RE_decoder_in_BF 7e-05
   

3. Evaluate

   (1) Generate predicted data for Blank Filling Module

   For WebNLG*:

   python RFBFN_generate_result.py  --generate_step "1"
   
   cd data_preprocess
   python preprocess.py --log_path "../pred_result/pred_data/" --step "2"
   

   For NYT*:

   python RFBFN_generate_result.py  --generate_step "1" --task "nyt"
   
   cd data_preprocess
   python preprocess.py --log_path "../pred_result/pred_data/" --step "2"  --task "nyt" --duplicate_questions 6
   

   (2) Obtain Performance

   For WebNLG*:

   python RFBFN_generate_result.py  --generate_step "2" 
   

   For NYT*:

   python RFBFN_generate_result.py  --generate_step "2" --task "nyt"
   

For WebNLG or NYT, add another --star 0 option and run the code similarly.