Presents the first online learning-to-defer algorithm with regret bounds O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
arXiv preprint arXiv:1611.01603 , year=
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Fine-tuning BERT for query-passage relevance classification achieves state-of-the-art results on TREC-CAR and MS MARCO, with a 27% relative gain in MRR@10 over prior methods.
TriviaQA is a new large-scale dataset for reading comprehension that features complex compositional questions, high lexical variability, and cross-sentence reasoning requirements, where current baselines reach only 40% while humans reach 80%.
MS MARCO is a new large-scale machine reading comprehension dataset built from real Bing search queries, human-generated answers, and web passages, supporting three tasks including answer synthesis and passage ranking.
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Online Learning-to-Defer with Varying Experts
Presents the first online learning-to-defer algorithm with regret bounds O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
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Passage Re-ranking with BERT
Fine-tuning BERT for query-passage relevance classification achieves state-of-the-art results on TREC-CAR and MS MARCO, with a 27% relative gain in MRR@10 over prior methods.
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
TriviaQA is a new large-scale dataset for reading comprehension that features complex compositional questions, high lexical variability, and cross-sentence reasoning requirements, where current baselines reach only 40% while humans reach 80%.
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
MS MARCO is a new large-scale machine reading comprehension dataset built from real Bing search queries, human-generated answers, and web passages, supporting three tasks including answer synthesis and passage ranking.