Neural Machine Translation via Binary Code Prediction

ACL 2017

Neural Machine Translation via Binary Code Prediction

Jan 22, 2021
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Abstract: In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English-Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10. Authors: Yusuke Oda, Philip Arthur, Graham Neubig, Koichiro Yoshino, Satoshi Nakamura (Nara Institute of Science and Technology, Carnegie Mellon University, Japan Science and Technology Agency)

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