added EACL 2023 video presentations #2606
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mjpost
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Jul 13, 2023
| @@ -66,6 +69,7 @@ | |||
| <abstract>Modern fact verification systems have distanced themselves from the black box paradigm by providing the evidence used to infer their veracity judgments. Hence, evidence-backed fact verification systems’ performance heavily depends on the capabilities of their retrieval component to identify these facts. A popular evaluation benchmark for these systems is the FEVER task, which consists of determining the veracity of short claims using sentences extracted from Wikipedia. In this paper, we present a novel approach to the the retrieval steps of the FEVER task leveraging the graph structure of Wikipedia. The retrieval models surpass state of the art results at both sentence and document level. Additionally, we show that by feeding our retrieved evidence to the best-performing textual entailment model, we set a new state of the art in the FEVER competition.</abstract> | |||
| <url hash="c2dfa529">2023.fever-1.4</url> | |||
| <bibkey>guzman-olivares-etal-2023-enhancing</bibkey> | |||
| <video href="2023.fever-1.4.mp4"/> | |||
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@davidstap this video doesn't load for me, can you comment?
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There was an issue with the video format. I converted it and it works now.
mjpost
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Jul 13, 2023
xinru1414
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Jul 29, 2023
Co-authored-by: Matt Post <mattpost@microsoft.com>
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