Improving Document-level Relation Extraction via Context Guided Mention Integration and Inter-pair Reasoning

The information of our mainly hardware environment is:

1. NVIDIA RTX 3090 24GB
2. CPU Intel(R) Xeon(R) Silver 4180 CPU @ 1.80GHz*32
3. RAM 32GB
4. CUDA (tested on 11.3)

The version of python is: Python 3.8.8 and the implementation steps are as follows:

Step 1. Install dependency python package

    pip install -r requirements.txt

Step 2. Prepare the dataset

The DocRED dataset can be downloaded following the instructions at link. The CDR and GDA datasets can be obtained following the instructions in edge-oriented graph. The expected structure of files is:

CGM2IR
|-- dataset
|    |-- docred
|    |    |-- train_annotated.json
|    |    |-- train_distant.json
|    |    |-- dev.json
|    |    |-- test.json
|    |    |-- rel_info.json
|    |    |-- label_map.json
|    |-- cdr
|    |    |-- train_filter.data
|    |    |-- dev_filter.data
|    |    |-- test_filter.data
|    |-- gda
|    |    |-- train.data
|    |    |-- dev.data
|    |    |-- test.data

Training and Evaluation

DocRED

Step 3. Process the DocRED

When finishing the step1 and step2, you can process the data by executing the following command:

sh scripts/process_bert.sh # for BERT
sh scripts/process_roberta.sh # for RoBERTa

Step 4. Training procedure

When finishing the step1, step2 and step3, you can train our proposed model by excuting the folloing command:
Train the BERT model on DocRED with the following command:

>> sh scripts/run_bert.sh  # for BERT
>> sh scripts/run_roberta.sh  # for RoBERTa

CDR and GDA

Train CDR and GDA model with the following command:

Step 3. Process the Dataset

When finishing the step1 and step2, you can process the data by executing the following command:
For CDR:

>> sh scripts/process_cdr.sh 

Or for GDA:

>> sh scripts/process_gda.sh 

Step 4.Training procedure

When finishing the step1, step2 and step3, you can train our proposed model by excuting the folloing command:
Train the BERT model on CDR or GDA with the following command: For CDR:

>> sh scripts/run_cdr.sh  # for sciBERT

Or for GDA:

>> sh scripts/run_gda.sh  # for sciBERT

The training loss and evaluation results on the dev set are synced to the wandb dashboard.

Saving and Evaluating Models

You can save the model by setting the --save_path argument before training. The model correponds to the best dev results will be saved. After that, You can evaluate the saved model by setting the --load_path argument, then the code will skip training and evaluate the saved model on benchmarks.