This work was developed by Luís Borges in the context of his M.Sc. thesis at Instituto Superior Técnico of the University of Lisbon. His work focused on the creation of a model to tackle the stance detection task proposed by the Fake News Challenge -- given a headline and the body of an article, to determine the stance of the headline relative to the body into one of {agrees, disagrees, discusses, unrelated}.
The source code in this repository is associated with Luís Borges' M.Sc. thesis and with the work described in the following publication:
@article{Borges2019,
author = {Borges, Luís and Martins, Bruno and Calado, Pável},
journal = {ACM Journal of Data Information and Quality},
title = {Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News},
year = {2019}
}
INSTRUCTIONS
1 - Download the SNLI and MultiNLI datasets from the two corresponding links:
https://nlp.stanford.edu/projects/snli/
http://www.nyu.edu/projects/bowman/multinli/
2 - Download and extract a set of features used as input to the model: https://mega.nz/#!08IyyKrT!rfsor4KQSPX0OCBAMhIzqhfOLkRZQBAN5BqqiIJXdrA
3 - Run python3 generate.py to generate necessary files for model training and testing. You can also download them directly:
https://mega.nz/#!tlAG3abL!hoPZhr7X5M1ifuPaXSAaNExuAz8DdEHHdV3EC5PSojE
4 - Run python3 nli-model.py to train and test the SNLI+MultiNLI model.
5 - Run python3 fnc-model.py to train and test the FNC model (code already includes SNLI+MultiNLI weight loading).
The source code was run with Python 3 and Keras 2.