Automated Fact-Checking of Claims in Argumentative Parliamentary Debates
Nona Naderi, Graeme Hirst · 2018
We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament.We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models.We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results.We further show that dodge statements can be detected with an F 1 measure as high as 82.57% in binary classification settings.