Uni-DUE Student Team: Tackling fact checking through decomposable attention neural network
This addresses the problem of automated fact verification for information retrieval systems, but it is incremental as it builds on existing challenge frameworks.
The paper tackles fact-checking by verifying claims using Wikipedia, achieving a FEVER score of 0.3927, which is a 25.5% improvement over the baseline.
In this paper we present our system for the FEVER Challenge. The task of this challenge is to verify claims by extracting information from Wikipedia. Our system has two parts. In the first part it performs a search for candidate sentences by treating the claims as query. In the second part it filters out noise from these candidates and uses the remaining ones to decide whether they support or refute or entail not enough information to verify the claim. We show that this system achieves a FEVER score of 0.3927 on the FEVER shared task development data set which is a 25.5% improvement over the baseline score.