R-BERT

(Unofficial) Pytorch implementation of R-BERT: Enriching Pre-trained Language Model with Entity Information for Relation Classification

Model Architecture

Method

1. Get three vectors from BERT.
   • [CLS] token vector
   • averaged entity_1 vector
   • averaged entity_2 vector
2. Pass each vector to the fully-connected layers.
   • dropout -> tanh -> fc-layer
3. Concatenate three vectors.
4. Pass the concatenated vector to fully-connect layer.
   • dropout -> fc-layer

• Exactly the SAME conditions as written in paper.
  • Averaging on entity_1 and entity_2 hidden state vectors, respectively. (including $, # tokens)
  • Dropout and Tanh before Fully-connected layer.
  • No [SEP] token at the end of sequence. (If you want add [SEP] token, give --add_sep_token option)

Dependencies

• perl (For evaluating official f1 score)
• python>=3.6
• torch==1.6.0
• transformers==3.3.1

How to run

$ python3 main.py --do_train --do_eval

• Prediction will be written on proposed_answers.txt in eval directory.

Official Evaluation

$ python3 official_eval.py
# macro-averaged F1 = 88.29%

• Evaluate based on the official evaluation perl script.
  • MACRO-averaged f1 score (except Other relation)
• You can see the detailed result on result.txt in eval directory.

Prediction

$ python3 predict.py --input_file {INPUT_FILE_PATH} --output_file {OUTPUT_FILE_PATH} --model_dir {SAVED_CKPT_PATH}

References

• Semeval 2010 Task 8 Dataset
• Semeval 2010 Task 8 Paper
• NLP-progress Relation Extraction
• Huggingface Transformers
• https://github.com/wang-h/bert-relation-classification