41 citations · 87 across the 24 of their papers we have counts for
5 papers · 1 filter
Rephrasing the Reference for Non-Autoregressive Machine Translation
Chenze Shao, Jinchao Zhang, Jie Zhou +1
Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the r…
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
Chenze Shao, Yang Feng
Non-autoregressive translation (NAT) models are typically trained with the cross-entropy loss, which forces the model outputs to be aligned verbatim with the target sentence and wi…
Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Chenze Shao, Zhengrui Ma, Yang Feng
Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA…
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{…