41 citations · 73 across the 10 of their papers we have counts for
10 papers
One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation
Chenze Shao, Xuanfu Wu, Yang Feng
Non-autoregressive neural machine translation (NAT) suffers from the multi-modality problem: the source sentence may have multiple correct translations, but the loss function is ca…
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation
Chenze Shao, Yang Feng
Neural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions. This problem is called \textit{…
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation
Yang Feng, Shuhao Gu, Dengji Guo +2
Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks gl…
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Chenze Shao, Yang Feng, Jinchao Zhang +2
In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregre…
Modeling Coverage for Non-Autoregressive Neural Machine Translation
Yong Shan, Yang Feng, Chenze Shao
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously. Despite its high efficiency, NAT usually suf…
Generating Diverse Translation from Model Distribution with Dropout
Xuanfu Wu, Yang Feng, Chenze Shao
Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate d…