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Aggregating pairwise semantic differences for few-shot claim verification

Xia Zeng, Arkaitz Zubiaga

PeerJ Computer Science. 2022.

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As part of an automated fact-checking pipeline, the claim verification 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 verification 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 claim verification 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.
 title = {Aggregating pairwise semantic differences for few-shot claim verification},
 author = {Zeng, Xia and Zubiaga, Arkaitz},
 year = 2022,
 month = oct,
 volume = 8,
 pages = {e1137},
 journal = {PeerJ Computer Science},
 issn = {2376-5992},
 url = {https://doi.org/10.7717/peerj-cs.1137},
 doi = {10.7717/peerj-cs.1137}