A Vector-Based Approach to Few-Shot Veracity Classification for Automated Fact-Checking

Abstract

As progress on automated fact-checking continues to be called, veracity classification has gained more attention. It is the task of predicting the veracity of a given claim by comparing it with retrieved pieces of evidence. One of the challenges for this task is to obtain manual annotations for large datasets, especially when it comes to new domains for which labelled data is unavailable in the first instance. In this paper, we describe a vector-based approach that achieves significant performance improvement on veracity classification in few-shot settings. Performance is compared with two competitive baselines: (1) fine-tuning BERT / RoBERTa, and (2) the state-of-the-art few-shot veracity classification approach leveraging language model perplexity with thresholds. Our approach first utilises sentence-BERT to get sentence vectors of claims and evidences. We then create a relation vector for each claim-evidences pairs, by applying absolute operation on their vector offsets. Experiments show significant improvements over the baselines.

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Reproductions