Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification

• URL: http://arxiv.org/abs/2205.05646v1
• Date: Wed, 11 May 2022 17:23:37 GMT
• Title: Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification
• Authors: Xia Zeng, Arkaitz Zubiaga
• Abstract summary: We introduce SEED, a novel vector-based method to claim veracity classification. We build on the hypothesis that we can simulate class representative vectors that capture average semantic differences for claim-evidence pairs in a class. Experiments conducted on the FEVER and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings.
• Score: 21.842139093124512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 that capture average semantic differences for claim-evidence pairs in a class, which can then be used for classification of new instances. We compare the performance of our method with competitive baselines including fine-tuned BERT/RoBERTa models, as well as the state-of-the-art few-shot veracity classification method that leverages language model perplexity. Experiments conducted on the FEVER and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available.

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