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WeChat Neural Machine Translation Systems for WMT20

arXiv 2010.00247 ↗First posted 1 Oct 2020cs.CL cs.AI cs.LGcited by 11

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Abstract (arXiv)

We participate in the WMT 2020 shared news translation task on Chinese to English. Our system is based on the Transformer (Vaswani et al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese to English system achieves 36.9 case-sensitive BLEU score, which is the highest among all submissions.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2020.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 1.

11 papers.

  1. 2021Selective Knowledge Distillation for Neural Machine Translationcited by 42
  2. 2021Bidimensional Leaderboards: Generate and Evaluate Language Hand in Handcited by 32
  3. 2021The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21cited by 14
  4. 2021WeChat Neural Machine Translation Systems for WMT21cited by 11
  5. 2021Scheduled Sampling Based on Decoding Steps for Neural Machine Translationcited by 11
  6. 2021Confidence-Aware Scheduled Sampling for Neural Machine Translationcited by 10
  7. 2022Onception: Active Learning with Expert Advice for Real World Machine Translationcited by 5
  8. 2021The NiuTrans Machine Translation Systems for WMT21cited by 4
  9. 2022BJTU-WeChat's Systems for the WMT22 Chat Translation Taskcited by 1
  10. 2022Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Taskcited by 0
  11. 2023Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluationcited by 0