Introduction

The repository was developed as a part of the Fake News Challenge Stage 1 (FNC-1 http://www.fakenewschallenge.org/) by team Athene: Andreas Hanselowski, Avinesh PVS, Benjamin Schiller and Felix Caspelherr. In the project, we worked in close collaboration with Debanjan Chaudhuri.

Our new paper in COLING 2018: A Retrospective Analysis of the Fake News Challenge Stance Detection Task

Our Blog Post on the Fake News Challenge.

Prof. Dr. Iryna Gurevych, AIPHES-Ubiquitous Knowledge Processing (UKP) Lab, TU-Darmstadt, Germany

Requirements

• Software dependencies

    python >= 3.4.0 (tested with 3.4.0)
  

Installation

1. Install required python packages.

    python3.4 -m pip install -r requirements.txt --upgrade
   

2. In order to reproduce the the results of our best submission to the FNC-1, please go to Athene_FNC-1 Google Drive and download the features.zip and model.zip and unzip them in respective folders.

    unzip  features.zip athene_system/data/fnc-1/features
    unzip  model.zip athene_system/data/fnc-1/mlp_models
   

3. Parts of the Natural Language Toolkit (NLTK) might need to be installed manually.

    python3.4 -c "import nltk; nltk.download('stopwords'); nltk.download('punkt'); nltk.download('wordnet')"
   

4. Copy Word2Vec GoogleNews-vectors-negative300.bin.gz in folder athene_system/data/embeddings/google_news/

5. Download Paraphrase Database: Lexical XL Paraphrases 1.0 and extract it to the ppdb folder.

    gunzip ppdb-1.0-xl-lexical.gz athene_system/data/ppdb/
   

6. To use the Stanford-parser an instance has to be started in parallel: Download Stanford CoreNLP, extract anywhere and execute following command:

    wget http://nlp.stanford.edu/software/stanford-corenlp-full-2016-10-31.zip
    java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9020
   

Additional notes

• In order to reproduce the classification results of the best submission at the day of the FNC-1, it is mandatory to use tensorflow v0.9.0 (ideally GPU version) and the exact library versions stated in requirements.txt, including python 3.4.

• Setup tested on Anaconda3 (tensorflow 0.9 gpu version)*

    conda create -n env_python3.4 python=3.4 anaconda
  
    source activate env_python3.4
  
    env_python3.4/bin/python3.4 -m pip install -r requirements.txt --upgrade
  
    env_python3.4/bin/python3.4 -m pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0rc0-cp34-cp34m-linux_x86_64.whl
  

To Run

To run the pre trained model and test

python pipeline.py -p ftest

For more details

python pipeline.py --help         
    
    e.g.: python pipeline.py -p crossv holdout ftrain ftest
    
    * crossv: runs 10-fold cross validation on train / validation set and prints the results
    * holdout: trains classifier on train and validation set, tests it on holdout set and prints the results
    * ftrain: trains classifier on train/validation/holdout set and saves it to athene_systems/data/fnc-1/mlp_models
    * ftest: predicts stances of unlabeled test set based on the model (see Installation, step 2) 

After ftest was executed, the labeled stances will be saved to disk:

cat athene_system/data/fnc-1/fnc_results/submission.csv

System description

A more detailed description of the system including the features, which have been used, can be found in the document: system_description_athene.pdf