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UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training
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We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA, except that it utilizes more supervision -- roughly 3x the number of datasets used for UnifiedQA. This generally leads to better in-domain and cross-domain results.
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Cited by 3 Pith papers
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Further pre-training BERT and RoBERTa on next-sentence polarity prediction and a polarity-reversing variant of next sentence prediction improves negation reasoning by 1.8 to 9.3 accuracy points on CondaQA.
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A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects
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