Microsoft Research Asia's Systems for WMT19
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Abstract (arXiv)
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place for the other three. Our basic systems are built on Transformer, back translation and knowledge distillation. We integrate several of our rececent techniques to enhance the baseline systems: multi-agent dual learning (MADL), masked sequence-to-sequence pre-training (MASS), neural architecture optimization (NAO), and soft contextual data augmentation (SCA).
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2019.
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Citing papers per year: 2021: 2; 2022: 6; 2023: 2.
10 papers.
- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
- 2023A Comprehensive Overview of Large Language Modelscited by 639
- 2021Survey of Low-Resource Machine Translationcited by 92
- 2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Modelcited by 57
- 2022Using natural language prompts for machine translationcited by 27
- 2022Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Modelscited by 9
- 2022Transcormer: Transformer for Sentence Scoring with Sliding Language Modelingcited by 8
- 2021Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effortcited by 7
- 2022Onception: Active Learning with Expert Advice for Real World Machine Translationcited by 5
- 2023Automatic Discrimination of Human and Neural Machine Translation in Multilingual Scenarioscited by 2
- 2023A Comprehensive Overview of Large Language Models12 Jul 2023
- 2023Automatic Discrimination of Human and Neural Machine Translation in Multilingual Scenarios31 May 2023
- 2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model2 Aug 2022
- 2022Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling25 May 2022
- 2022PaLM: Scaling Language Modeling with Pathways5 Apr 2022
- 2022Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models1 Apr 2022
- 2022Onception: Active Learning with Expert Advice for Real World Machine Translation9 Mar 2022
- 2022Using natural language prompts for machine translation23 Feb 2022
- 2021Survey of Low-Resource Machine Translation1 Sep 2021
- 2021Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort27 May 2021