LongChecker: Improving scientific claim verification by modeling full-abstract context
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We introduce the LongChecker system for scientific claim verification. Given a scientific claim and an evidence-containing research abstract, LongChecker predicts a veracity label and identifies supporting rationales in a multitask fashion based on a shared encoding of the claim and abstract. We perform experiments on the SciFact dataset, and find that LongChecker achieves state-of-the-art performance. We conduct analysis to understand the source of this improvement, and find that identifying the relationship between a claim and a rationale reporting a scientific finding often requires understanding the context in which the rationale appears. By making labeling decisions based on all available context, LongChecker achieves better performance on cases requiring this type of understanding. In addition, we show that LongChecker is able to leverage weakly-supervised in-domain data to facilitate few-shot domain adaptation for scientific claim verification.
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