Glancing Transformer for Non-Autoregressive Neural Machine Translation

• URL: http://arxiv.org/abs/2008.07905v3
• Date: Thu, 13 May 2021 14:41:40 GMT
• Title: Glancing Transformer for Non-Autoregressive Neural Machine Translation
• Authors: Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li
• Abstract summary: We propose a method to learn word interdependency for single-pass parallel generation models. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8-15 times speedup.
• Score: 58.87258329683682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM), a method to learn word interdependency for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8-15 times speedup. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points.

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