| Autor: |
Xia Zeng, Arkaitz Zubiaga |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
PeerJ Computer Science, Vol 8, p e1137 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2376-5992 |
| DOI: |
10.7717/peerj-cs.1137 |
| Popis: |
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 article, we introduce Semantic Embedding Element-wise Difference (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 Bidirectional Encoder Representations from Transformers (BERT)/Robustly Optimized BERT Pre-training Approach (RoBERTa) models, as well as the state-of-the-art few-shot claim verification method that leverages language model perplexity. Experiments conducted on the Fact Extraction and VERification (FEVER) and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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