Evidence Selection as a Token-Level Prediction Task
Dominik Stammbach · 2021
In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim.Intuitively, the retrieval of the correct evidence plays a crucial role in this process.Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim.In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once.We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER.Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark.