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The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task ...
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Uncovering Main Causalities for Long-tailed Information Extraction ...
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Stance Detection in German News Articles
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In: Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER) (2021)
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Evidence Selection as a Token-Level Prediction Task
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In: Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER) (2021)
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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.
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URL: https://hdl.handle.net/20.500.11850/521291 https://doi.org/10.3929/ethz-b-000521291
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FANG-COVID: A new large-scale benchmark dataset for fake news detection in German
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Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 297-312 (2019) (2019)
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