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Echo State Neural Machine Translation

2020-02-27 00:08:45
Ankush Garg, Yuan Cao, Qi Ge
arXiv_CL
     
Abstract

We present neural machine translation (NMT) models inspired by echo state network (ESN), named Echo State NMT (ESNMT), in which the encoder and decoder layer weights are randomly generated then fixed throughout training. We show that even with this extremely simple model construction and training procedure, ESNMT can already reach 70-80% quality of fully trainable baselines. We examine how spectral radius of the reservoir, a key quantity that characterizes the model, determines the model behavior. Our findings indicate that randomized networks can work well even for complicated sequence-to-sequence prediction NLP tasks.

Abstract (translated)

URL

https://arxiv.org/abs/2002.11847

PDF

https://arxiv.org/pdf/2002.11847.pdf

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