activity
20162026
most citedUsing DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

299 citations · 1.1k across the 29 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.CL2021★ 15 cited

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…

cs.CL2021

Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training

Bo Zheng, Li Dong, Shaohan Huang +5

Compared to monolingual models, cross-lingual models usually require a more expressive vocabulary to represent all languages adequately. We find that many languages are under-repre…

cs.CL2021★ 5 cited

Consistency Regularization for Cross-Lingual Fine-Tuning

Bo Zheng, Li Dong, Shaohan Huang +7

Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine…

cs.CL2021

Language Scaling for Universal Suggested Replies Model

Qianlan Ying, Payal Bajaj, Budhaditya Deb +7

We consider the problem of scaling automated suggested replies for Outlook email system to multiple languages. Faced with increased compute requirements and low resources for langu…

cs.CL2021★ 24 cited

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…

cs.CL2021★ 51 cited

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…

cs.CL2021★ 128 cited

COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

Yu Meng, Chenyan Xiong, Payal Bajaj +4

We present a self-supervised learning framework, COCO-LM, that pretrains Language Models by COrrecting and COntrasting corrupted text sequences. Following ELECTRA-style pretraining…