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1
SMDT: Selective Memory-Augmented Neural Document Translation ...
Zhang, Xu; Yang, Jian; Huang, Haoyang. - : arXiv, 2022
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2
StableMoE: Stable Routing Strategy for Mixture of Experts ...
Dai, Damai; Dong, Li; Ma, Shuming. - : arXiv, 2022
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3
DeepNet: Scaling Transformers to 1,000 Layers ...
Wang, Hongyu; Ma, Shuming; Dong, Li. - : arXiv, 2022
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4
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt ...
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5
On the Representation Collapse of Sparse Mixture of Experts ...
Chi, Zewen; Dong, Li; Huang, Shaohan. - : arXiv, 2022
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6
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model ...
Sun, Xin; Ge, Tao; Ma, Shuming. - : arXiv, 2022
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7
Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation ...
Chen, Guanhua; Ma, Shuming; Chen, Yun. - : arXiv, 2021
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8
Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders ...
Chen, Guanhua; Ma, Shuming; Chen, Yun. - : arXiv, 2021
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9
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs ...
Chi, Zewen; Dong, Li; Ma, Shuming. - : arXiv, 2021
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10
Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task ...
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11
DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders ...
Ma, Shuming; Dong, Li; Huang, Shaohan. - : arXiv, 2021
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12
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA ...
Chi, Zewen; Huang, Shaohan; Dong, Li. - : arXiv, 2021
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13
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ...
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14
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders ...
Abstract: Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialized Transformer backbone. In this work, inspired by the recent success of language model pre-training, we present XLM-T, which initializes the model with an off-the-shelf pretrained cross-lingual Transformer encoder and fine-tunes it with multilingual parallel data. This simple method achieves significant improvements on a WMT dataset with 10 language pairs and the OPUS-100 corpus with 94 pairs. Surprisingly, the method is also effective even upon the strong baseline with back-translation. Moreover, extensive analysis of XLM-T on unsupervised syntactic parsing, word alignment, and multilingual classification explains its effectiveness for machine translation. The code will be at https://aka.ms/xlm-t. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2012.15547
https://dx.doi.org/10.48550/arxiv.2012.15547
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15
Deconvolution-Based Global Decoding for Neural Machine Translation ...
Lin, Junyang; Sun, Xu; Ren, Xuancheng. - : arXiv, 2018
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16
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification ...
Ma, Shuming; Sun, Xu. - : arXiv, 2017
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