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A Tensorized Transformer for Language Modeling
[article]
2019
arXiv
pre-print
Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, has led to breakthroughs in Natural Language Processing (NLP) tasks. However, the multi-head attention mechanism, as a key component of Transformer, limits the effective deployment of the model to a resource-limited setting. In this paper, based on the ideas of tensor decomposition and parameters sharing, we propose a
arXiv:1906.09777v3
fatcat:2o7u4242wbfipixrjotrv47eby