2019
EMNLP
EMNLP 2019
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task
Abstract
AbstractThis paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge. We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. We use a publicly available document retrieval module and have fine-tuned BERT checkpoints for sentence se- lection and as the entailment classifier. We report a FEVER score of 68.46% on the blind testset.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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Trend Setter
— Fine-Tuning
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Keyword Pioneer
— entailment classification
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Hot Topic Early Bird
— fact verification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio