Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification. (arXiv:2205.05646v1 [cs.CL])

As part of an automated fact-checking pipeline, the claim veracity
classification task consists in determining if a claim is supported by an
associated piece of evidence. The complexity of gathering labelled
claim-evidence pairs leads to a scarcity of datasets, particularly when dealing
with new domains. In this paper, we introduce SEED, a novel vector-based method
to few-shot claim veracity classification that aggregates pairwise semantic
differences for claim-evidence pairs. We build on the hypothesis that we can
simulate class representative vectors …

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