mlsanityData to 14 Oct 2026

Target-Side Augmentation for Document-Level Machine Translation

arXiv 2305.04505 ↗First posted 8 May 2023cs.CLcited by 5

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Abstract (arXiv)

Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks. Our code is available at https://github.com/baoguangsheng/target-side-augmentation.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 43 of its 69 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

43 of 69 references.

References · 43

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  5. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  6. 2020Knowledge Distillation: A Surveycited by 1,603
  7. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  8. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 927
  9. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  10. 2016Sequence-Level Knowledge Distillationcited by 801
  11. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  12. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 639
31 more
  1. 2013Do Deep Nets Really Need to be Deep?cited by 474
  2. 2019PAWS: Paraphrase Adversaries from Word Scramblingcited by 402
  3. 2020Rethinking Pre-training and Self-trainingcited by 352
  4. 2017Six Challenges for Neural Machine Translationcited by 335
  5. 2019Revisiting Self-Training for Neural Sequence Generationcited by 155
  6. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  7. 2021Gender Bias in Machine Translationcited by 117
  8. 2018Conditional BERT Contextual Augmentationcited by 107
  9. 2018Document-Level Neural Machine Translation with Hierarchical Attention Networkscited by 85
  10. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  11. 2016Reward Augmented Maximum Likelihood for Neural Structured Predictioncited by 81
  12. 2020Autoregressive Knowledge Distillation through Imitation Learningcited by 74
  13. 2018SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translationcited by 72
  14. 2019Selective Attention for Context-aware Neural Machine Translationcited by 60
  15. 2021The paradox of the compositionality of natural language: a neural machine translation case studycited by 59
  16. 2019Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translationcited by 56
  17. 2021G-Transformer for Document-level Machine Translationcited by 46
  18. 2016Variational Neural Machine Translationcited by 35
  19. 2020Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversitycited by 35
  20. 2021On Compositional Generalization of Neural Machine Translationcited by 34
  21. 2020Rethinking Document-level Neural Machine Translationcited by 32
  22. 2019Domain, Translationese and Noise in Synthetic Data for Neural Machine Translationcited by 26
  23. 2020Simulated Multiple Reference Training Improves Low-Resource Machine Translationcited by 22
  24. 2018Conditional Variational Autoencoder for Neural Machine Translationcited by 20
  25. 2019Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translationcited by 16
  26. 2022Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translationcited by 16
  27. 2018Sequence to Sequence Mixture Model for Diverse Machine Translationcited by 13
  28. 2021Contextualized Rewriting for Text Summarizationcited by 8
  29. 2020Towards Making the Most of Context in Neural Machine Translationcited by 7
  30. 2022A General Contextualized Rewriting Framework for Text Summarizationcited by 3
  31. 2022SMDT: Selective Memory-Augmented Neural Document Translationcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translationcited by 10
  2. 2024DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translatorscited by 6
  3. 2024Context-Aware Machine Translation with Source Coreference Explanationcited by 2
  4. 2023Non-Autoregressive Document-Level Machine Translationcited by 1
  5. 2025Interpretable Style Takagi-Sugeno-Kang Fuzzy Clusteringcited by 0