/RSL_analysis

Primary LanguagePython

LSR

This repository is the PyTorch implementation of our LSR model with GloVe embeddings in ACL 2020 Paper "Reasoning with Latent Structure Refinement for Document-Level Relation Extraction".

Requirement

python==3.6.7 
torch==1.3.1 + CUDA == 9.2 1.5.1
OR torch==1.5.1 + CUDA == 10.1
tqdm==4.29.1
numpy==1.15.4
spacy==2.1.3
networkx==2.4

Overview of LSR

Node construtor:

Node Constructor Overview of the Node Constructor: A context encoder is applied to get the contextualized representations of sentences. The representations of mentions and words in the meta dependency path are extracted as mention nodes and MDP nodes. An average pooling is used to construct the entity node from the mention nodes. For example, the entity node Lutsenko is constructed by averaging representations of its mentions Lutsenko and He.

Dynamic Reasoner:

Overview of the Dynamic Reasoner. Each block consists of two sub-modules: structure induction and multi-hop reasoning. The first module takes the nodes constructed by the Node Constructor as inputs. Representations of nodes are fed into two feed forward networks before the bilinear transformation. The latent document-level structure is computed by the Matrix-Tree Theorem. The second module takes the structure as input and updates representations of nodes by using the densely connected graph convolutional networks. We stack $N$ blocks which correspond to $N$ times of refinement. Each iteration outputs the latent structure for inference.

Dataset

For the dataset and pretrained embeddings, please download it here, which are officially provided by DocRED: A Large-Scale Document-Level Relation Extraction Dataset .

Data Proprocessing

After you download the dataset, please put the files train_annotated.json, dev.json and test.json to the ./data directory, and files in pre directory to the code/prepro_data. Run:

# cd code
# python3 gen_data.py

Training

In order to train the model, run:

# cd code
# python3 train.py

Test

After the training process, we can test the model by:

python3 test.py

Related Repo

Codes are adapted from the repo of the ACL2019 paper DocRED DocRED: A Large-Scale Document-Level Relation Extraction Dataset.

Citation

@inproceedings{nan2020lsr,
 author = {Guoshun, Nan and Zhijiang, Guo and  Ivan, Sekulić and Wei, Lu},
 booktitle = {Proc. of ACL},
 title = {Reasoning with Latent Structure Refinement for Document-Level Relation Extraction},
 year = {2020}
}