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Neural Language Modeling With Implicit Cache Pointers
[article]
2020
arXiv
pre-print
A cache-inspired approach is proposed for neural language models (LMs) to improve long-range dependency and better predict rare words from long contexts. This approach is a simpler alternative to attention-based pointer mechanism that enables neural LMs to reproduce words from recent history. Without using attention and mixture structure, the method only involves appending extra tokens that represent words in history to the output layer of a neural LM and modifying training supervisions
arXiv:2009.13774v1
fatcat:64dzobqqh5h4ffw27dxof47roy