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20212026
most citedSelf-Distillation Mixup Training for Non-autoregressive Neural Machine Translation

8 citations · 15 across the 6 of their papers we have counts for

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cs.CL20242 cited

UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction

Jiaxin Guo, Minghan Wang, Xiaosong Qiao +9

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works u…

cs.CL2023

Text Style Transfer Back-Translation

Daimeng Wei, Zhanglin Wu, Hengchao Shang +6

Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the transla…

cs.CL20215 cited

Diformer: Directional Transformer for Neural Machine Translation

Minghan Wang, Jiaxin Guo, Yuxia Wang +8

Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current…

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.CL20218 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…