T5 casts all NLP tasks as text-to-text generation, systematically explores pre-training choices, and reaches strong performance on summarization, QA, classification and other tasks via large-scale training on the Colossal Clean Crawled Corpus.
Long short-term memory-networks for machine reading.arXiv preprint arXiv:1601.06733
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Graph Attention Networks compute learnable attention coefficients over node neighborhoods to produce weighted feature aggregations, achieving state-of-the-art results on citation networks and inductive protein-protein interaction graphs.
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.
Pointer sentinel-LSTM mixes context copying with softmax prediction to reach 70.9 perplexity on Penn Treebank using fewer parameters than standard LSTMs.
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
T5 casts all NLP tasks as text-to-text generation, systematically explores pre-training choices, and reaches strong performance on summarization, QA, classification and other tasks via large-scale training on the Colossal Clean Crawled Corpus.