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20162023
most citedExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

3.7k citations · 8.3k across the 22 of their papers we have counts for

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Showing 2017Show all

2 papers · 1 filter

cs.CL2017★ 110 cited

Get To The Point: Summarization with Pointer-Generator Networks

Abigail See, Peter J. Liu, Christopher D. Manning

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passa…

cs.LG2017★ 225 cited

Online and Linear-Time Attention by Enforcing Monotonic Alignments

Colin Raffel, Minh-Thang Luong, Peter J. Liu +2

Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft at…