jimp93 / detecting-incongruity

TensorFlow implementation of "Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder," AAAI-19

detecting-incongruity

This repository contains the source code & data corpus used in the following paper,

Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder, AAAI-19, paper

Requirements

  tensorflow==1.4 (tested on cuda-8.0, cudnn-6.0)
  python==2.7
  scikit-learn==0.20.0
  nltk==3.3

Download Dataset

• download preprocessed dataset with the following script

  cd data
  sh download_processed_dataset_aaai-19.sh

• the downloaded dataset will be placed into the following path of the project

  /data/aaai-19/para
  /data/aaai-19/whole

• format (example)

  test_title.npy: [100000, 49] - (#samples, #token (index))
  test_body: [100000, 1200] - (#samples, #token (index))
  test_label: [100000] - (#samples)
  dic_mincutN.txt: dictionary

Source Code

• according to the training method

  whole-type: using the codes in the ./src_whole
  para-type: using the codes in the ./src_para
  

Training Phase

• each source code folder contains a reference script for training the model

  train_reference_scripts.sh
  << for example >>
  train dataset with AHDE model and "whole" method
  

python AHDE_Model.py --batch_size 256 --encoder_size 80 --context_size 10 --encoderR_size 49 --num_layer 1 --hidden_dim 300  --num_layer_con 1 --hidden_dim_con 300 --embed_size 300 --lr 0.001 --num_train_steps 100000 --is_save 1 --graph_prefix 'ahde' --corpus 'aaai-19_whole' --data_path '../data/target_aaai-19_whole/'

• Results will be displayed in the console
  
• The final test result will be stored in "./TEST_run_result.txt"
  

※ hyper parameters

• major parameters: edit from the training script
  
• other parameters: edit from "./params.py"

Inference Phase

• each source code folder contains an inference script
• you need to modify the "model_path" in the "eval_AHDE.sh" to a proper path

  << for example >>
  evaluate test dataset with AHDE model and "whole" method
  

	src_whole$ sh eval_AHDE.sh

• Results will be displayed in the console
  
• scores for the testset will be stored in "./output.txt"
  

Dataset Statistics

• whole case
  

  data	Samples	tokens (avg) headline	tokens (avg) body text
  train	1,700,000	13.71	499.81
  dev	100,000	13.69	499.03
  test	100,000	13.55	769.23

• Note
  

  We crawled articles for "dev" and "test" dataset from different media outlets.
  

Newly introduced dataset (English version)

• We create an English version of the dataset, nela-17, using NELA 2017 data. Please refer to the dataset repository [link].
• If you want to run our model (AHDE) with the nela-17 data, you can use the preprocessed dataset that is compatible with our code.

  cd data
  sh download_processed_dataset_nela-17.sh

• training script (refer to the "train_reference_scripts.sh")

python AHDE_Model.py --batch_size 64 --encoder_size 200 --context_size 50 --encoderR_size 25 --num_layer 1 --hidden_dim 100  --num_layer_con 1 --hidden_dim_con 100 --embed_size 300 --use_glove 1 --lr 0.001 --num_train_steps 100000 --is_save 1 --graph_prefix 'ahde' --corpus 'nela-17_whole' --data_path '../data/target_nela-17_whole/'

Other implementation (pytorch version)

• Pytorch implementation [link] by M. Lee
• compatible with the preprocessed dataset

cite

• Please cite our paper, when you use our code | dataset | model

@inproceedings{yoon2019detecting,
title={Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder},
author={Yoon, Seunghyun and Park, Kunwoo and Shin, Joongbo and Lim, Hongjun and Won, Seungpil and Cha, Meeyoung and Jung, Kyomin},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={33},
pages={791--800},
year={2019}
}