Echo State Neural Machine Translation

• URL: http://arxiv.org/abs/2002.11847v1
• Date: Thu, 27 Feb 2020 00:08:45 GMT
• Title: Echo State Neural Machine Translation
• Authors: Ankush Garg, Yuan Cao, and Qi Ge
• Abstract summary: We present neural machine translation (NMT) models inspired by echo state network (ESN), named Echo State NMT (ESNMT) We show that even with this extremely simple model construction and training procedure, ESNMT can already reach 70-80% quality of fully trainable baselines.
• Score: 7.496705711191467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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