Awesome

DIAG-NRE

Source code for ACL 2019 paper "DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction".

Overview

DIAG-NRE aims to establish a bridge between Distant Supervision (DS) and Weak Label Fusion (WLF) for Neural Relation Extraction (NRE), which generally contains two key stages:

• Pattern Extraction, generating patterns automatically based on reinforcement learning (RL);
• Pattern Refinement, refining patterns with the help of human annotations on a small set of actively selected instances.

Advantages of DIAG-NRE include:

• denoising distantly supervised labels with reduced human skill requirements;
• enabling quicker generalization to new relation types;
• interpreting which features neural models have learned.
• interpreting from what kinds of noises the target relation type suffers.

Citation

If you find our work interesting, you can cite the paper as

@inproceedings{zheng2019diagnre,
               title={DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction},
               author={Zheng, Shun and Han, Xu and Lin, Yankai and Yu, Peilin and Chen, Lu and Huang, Ling and Liu, Zhiyuan and Xu, Wei},
               booktitle={ACL},
               year={2019}
}

Setup

1. Run conda env create --file=environment.yml to prepare a basic python environment.

2. Then, when starting a fresh shell, please run the following codes first.

# activate the conda env
source activate diag-nre
# set proper environment variables, including 'PATH', 'PYTHONPATH', 'WORK_DIR', etc.
source ./shell/set_env.sh

set_env.sh will help to set proper environment variables, and create logs and model directories when needed.

Data Preparation

We have preprocessed the NYT dataset and the UW as mentioned in the paper.

1. Download our preprocessed data collection associated with human diagnostic annotations, base_data.zip, into the current directory.

2. Run the following codes to prepare well-formatted data for subsequent model training

unzip base_data.zip
cd base_data
# 'WORK_DIR' will be set properly after executing 'source ./shell/set_env.sh'
python prepare_data.py $WORK_DIR

DIAG-NRE

0. Training NRE Models

Run python batch_train_rel.py, then the program will automatically bind idle gpus and train NRE models for all 14 relations.

1. Pattern Extraction

Run python batch_train_agent.py, then the program will train pattern-extraction agents for all 14 relations by interacting with well-trained NRE models.

2. Pattern Refinement + Weak Label Fusion (WLF)

Run python batch_train_diag.py, then the program will

• conduct the pattern refinement based on human diagnostic annotations;
• produce denoised training labels by fusing multiple weak supervision sources;

Here, we already provide our annotations based on the actively selecting process described in the paper.

For users who need to apply DIAG-NRE to other data, you can extract instances to be annotated from the pattern hierarchy file, named as 'rule_hierarchy_model{}.pkl'.

Example codes to check the pattern hierarchy

# load hierarchy
import pickle
with open('rule_hierarchy_file_path', 'rb') as fin:
    pattern_hierarchy = pickle.load(fin)

# print hierarchy
from rule_helpers import print_rule_hierarchy
print_rule_hierarchy(pattern_hierarchy)

Note that, in this code, the term 'rule' shares the same notion with the term 'pattern' used in the paper.

3. Retraining & Evaluation

Run python batch_retrain_rel.py to retrain multiple NRE models with different random seeds for all kinds of labels.

Run python batch_eval_total.py to aggregate all evaluation results.

File Specifications

Log

• log.relXXX, the log for NRE model training
• log.agentXXX, the log for agent network training and pattern generation
• log.diagXXX, the log for the pattern refinement
• log.eval_totalXXX, the log for the final total evaluation

Model

• erasure_policy_modelXXX, the checkpoint of the agent network;
• rel_modelXXX, the checkpoint of the NRE model;
• train_erasure_decision_modelXXX, the pickled file for agent's decisions;
• rule_info_modelXXX, the pickled file storing pattern information derived from certain decision files;
• rule_hierarchy_modelXXX, the pickled file storing the constructed pattern hierarchy merged from multiple pattern information sources.