Papers › Multilingual Non-Autoregressive Machine Translation without Knowledge Distillation
Multilingual Non-Autoregressive Machine Translation without Knowledge Distillation
Chenyang Huang, Fei Huang, Zaixiang Zheng, Osmar R. Zaïane, Hao Zhou, Lili Mou
Multilingual neural machine translation (MNMT) aims at using one single model for multiple translation directions. Recent work applies non-autoregressive Transformers to improve the efficiency of MNMT, but requires expensive knowledge distillation (KD) processes. To this end, we propose an M-DAT approach to non-autoregressive multilingual machine translation. Our system leverages the recent advance of the directed acyclic Transformer (DAT), which does not require KD. We further propose a pivot back-translation (PivotBT) approach to improve the generalization to unseen translation directions. Experiments show that our M-DAT achieves state-of-the-art performance in non-autoregressive MNMT.
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