8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 1 cited
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion
Hengchao Shang, Zongyao Li, Daimeng Wei +5
Computer-aided translation (CAT) aims to enhance human translation efficiency and is still important in scenarios where machine translation cannot meet quality requirements. One fu…
cs.CL2021
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Zhengzhe Yu, Jiaxin Guo, Minghan Wang +11
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encod…
cs.CL2021★ 8 cited
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
Jiaxin Guo, Minghan Wang, Daimeng Wei +11
Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performi…