164 citations · 853 across the 53 of their papers we have counts for
8 papers · 1 filter
Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task
Jian Yang, Shuming Ma, Haoyang Huang +8
This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tra…
Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
Guanhua Chen, Shuming Ma, Yun Chen +4
This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neu…
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
Zewen Chi, Shaohan Huang, Li Dong +8
In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token de…
DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders
Shuming Ma, Li Dong, Shaohan Huang +6
While pretrained encoders have achieved success in various natural language understanding (NLU) tasks, there is a gap between these pretrained encoders and natural language generat…
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?
Weijia Xu, Shuming Ma, Dongdong Zhang +1
While non-autoregressive (NAR) models are showing great promise for machine translation, their use is limited by their dependence on knowledge distillation from autoregressive mode…
Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
Guanhua Chen, Shuming Ma, Yun Chen +5
Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine tran…
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
Zewen Chi, Li Dong, Shuming Ma +3
Multilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve…
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma +7
Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sente…