Source code for Coling 2020 paper Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction

Method

Document-level Relation Extraction (RE) is particularly challenging due to complex se-mantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for doc-ument-level RE. However, traditional GCN simply takes word nodes and adjacency ma-trix to represent graphs, which is difficult to establish direct connections between distant entity pairs. In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of enti-ties in a document. Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account. Meantime, we leverage a large-scale distantly supervised da-ta to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated data for further improving document-level RE performance. The exper-imental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result.

Environments

• Ubuntu-18.10.1( 4.18.0-25-generic)
  
• Python(3.6.8)
  
• Cuda(10.1.243)
  

Dependencies

• matplotlib (3.3.2)
  
• networkx (2.4)
  
• nltk (3.4.5)
  
• numpy (1.19.2)
  
• torch (1.3.0)
  

Data

First you should get pretrained Bert_base model from huggingface and put it into ./bert/bert-base-uncased/.
Before running our code you need to obtain the DocRED dataset from the author of the dataset, Here.
After downing DocRED, you can use gen_data_extend_graph.py to preprocess data for GCGCN_Glove and use gen_bert_data_extend_graph.py to preprocess data for GCGCN_Bert. Finally, processed data will be saved into ./prepro_data and ./prepro_data_bert respectively.

Run code

train.py used to start training
test.py used to evaluation model's performance on Dev or Test set.
Config.py is for training Glove-based model And Config_bert.py is used for training Bert_based model

Evaluation

For Dev set, you can use test.py to evaluate you trained model. For Test set, you should first use test.py to get test results which saved in ./result, and submit it into Condalab competition.

Contacts

If you have any questions for code, please feel free to contact Yibin Xu(19xyb@mail.dlut.edu.cn), we will reply it as soon as possible.