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This paper proposes a novel procedure for training an encoder-decoder baseddeep neural network which compresses NxM models into a single model enabling usto dynamically choose the number of encoder and decoder layers for decoding.Usually, the output of the last layer of the N-layer encoder is fed to theM-layer decoder, and the output of the last decoder layer is used to computesoftmax loss. Instead, our method computes a single loss consisting of NxMlosses: the softmax loss for the output of each of the M decoder layers derivedusing the output of each of the N encoder layers. A single model trained by ourmethod can be used for decoding with an arbitrary fewer number of encoder anddecoder layers. In practical scenarios, this (a) enables faster decoding withinsignificant losses in translation quality and (b) alleviates the need totrain NxM models, thereby saving space. We take a case study of neural machinetranslation and show the advantage and give a cost-benefit analysis of ourapproach.

Multi-Layer Softmaxing during Training Neural Machine Translation for Flexible Decoding with Fewer Layers