A Tensorized Transformer for Language Modeling

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 novel self-attention model (namely Multi-linear attention) with Block-Term Tensor Decomposition (BTD). We test and verify the proposed attention method on three language modeling tasks (i. e. , PTB, WikiText-103 and One-billion) and a neural machine translation task (i. e. , WMT-2016 English-German). Multi-linear attention can not only largely compress the model parameters but also obtain performance improvements, compared with a number of language modeling approaches, such as Transformer, Transformer-XL, and Transformer with tensor train decomposition.

Authors

• Xindian Ma Tianjin University
• Peng Zhang
• Shuai Zhang New Jersey Institute of Technology
• Nan Duan Microsoft
• Yuexian Hou
• Ming Zhou Langboat Technology, Beijing, China
• Dawei Song

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