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Training Recurrent Neural Network through Moment Matching for NLP Applications
2018
Interspeech 2018
Recurrent neural network (RNN) is conventionally trained in the supervised mode but used in the free-running mode for inferences on testing samples. The supervised mode takes ground truth token values as RNN inputs but the free-running mode can only use self-predicted token values as surrogating inputs. Such inconsistency inevitably results in poor generalizations of RNN on out-of-sample data. We propose a moment matching (MM) training strategy to alleviate such inconsistency by simultaneously
doi:10.21437/interspeech.2018-1369
dblp:conf/interspeech/DengSCJ18
fatcat:lvw3w2s22bgnxloeyoxuctsufi