Open access
Author
Date
2021-11Type
- Conference Paper
ETH Bibliography
yes
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Abstract
In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim. Intuitively, the retrieval of the correct evidence plays a crucial role in this process. Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim. In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once. We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER. Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000521291Publication status
publishedExternal links
Editor
Book title
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)Pages / Article No.
Publisher
Association for Computational LinguisticsEvent
Organisational unit
09627 - Ash, Elliott / Ash, Elliott
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ETH Bibliography
yes
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