ss-abl's Introduction

Semi-Supervised Abductive Learning for Theft Judicial Sentencing

This is the repository for holding the sample code of the Semi-Supervised Abductive Learning framework for Theft Judicial Sentencing experiments in Semi-Supervised Abductive Learning and Its Application to Theft Judicial Sentencing in ICDM 2020.

This code is only tested in Linux environment.

Dependency

Python 3.6
tensorflow 1.12.0

Running Code

Set Data File Path

# Before running code, we should set data file path first.
# By default, all data files are in "./data/"

# Data file path's setting codes are lies on line 207 - 212 in "ss_abl_model.py".
# Bert Pretrain json file name
pretrain_filename = "0_0.10.json"

# Sentence model supervised traning data file path
pretrain_money_filename = "./data/0_0.10.csv" 

# Unlabeled data file name
abl_train_filename = "1_0.90.json"
abl_train_money_filename = "1_0.90.csv"

# Bert test data file name
test_filename = "10.json"
# Sentence model test data file name
test_money_filename = "10.csv"

unzip data/dataset.zip
wget https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip
unzip chinese_L-12_H-768_A-12.zip
python ss_abl_model.py

Parameters

Our model's parameters are listed below.

# Model's Parameter and default value
abl_max_change_num = 2 # The max number of label could be changed on abduction

abl_times = 1 # The model traning iteration number
rule_file_path = "rule_test_file.txt" # abduction rule file path

pretrain_bert_train_epochs = 16 # The epoch number of BERT training on Supervised data
pretrain_sentence_model_times = 3 # The epoch number of sentence model traning on Supervised data
abl_bert_train_epochs = 1 # The epoch number per iteration of BERT traning on abduction process
abl_sentence_model_times = 3 # The epoch number per iteration of sentence model traning on abduction process

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