FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn
  Correction in the Loop
  Fact-checking on the Web has become the main mechanism through which we
detect the credibility of the news or information. Existing fact-checkers
verify the authenticity of the information (support or refute the claim) based
on secondary sources of information. However, existing approaches do not
consider the problem of model updates due to constantly increasing training
data due to user feedback. It is therefore important to conduct user studies to
correct models' inference biases and improve the model in a life-long learning
manner in the future according to the user feedback. In this paper, we present
FaxPlainAC, a tool that gathers user feedback on the output of explainable
fact-checking models. FaxPlainAC outputs both the model decision, i.e., whether
the input fact is true or not, along with the supporting/refuting evidence
considered by the model. Additionally, FaxPlainAC allows for accepting user
feedback both on the prediction and explanation. Developed in Python,
FaxPlainAC is designed as a modular and easily deployable tool. It can be
integrated with other downstream tasks and allowing for fact-checking human
annotation gathering and life-long learning.
