mlsanityData to 14 Oct 2026

Learning a Multi-Domain Curriculum for Neural Machine Translation

arXiv 1908.10940 ↗First posted 28 Aug 2019cs.CL cs.LGcited by 16

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Abstract (arXiv)

Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Aug 2019.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 4; 2024: 4; 2025: 1; 2026: 1.

16 papers.

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  1. 2021Curriculum Learning: A Surveycited by 259
  2. 2022CLNode: Curriculum Learning for Node Classificationcited by 24
  3. 2022Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimizationcited by 18
  4. 2021Auxiliary Task Update Decomposition: The Good, The Bad and The Neutralcited by 17
  5. 2023In-Context Demonstration Selection with Cross Entropy Differencecited by 17
  6. 2025IRGPT: Understanding Real-world Infrared Image with Bi-cross-modal Curriculum on Large-scale Benchmarkcited by 12
  7. 2023Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentationcited by 7
  8. 2026Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVRcited by 5
  9. 2021Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarizationcited by 4
  10. 2024Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Taskcited by 4
  11. 2022DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translationcited by 2
  12. 2023BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognitioncited by 2
4 more
  1. 2024Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translationcited by 1
  2. 2024HW-TSC's Submission to the CCMT 2024 Machine Translation Taskscited by 1
  3. 2023Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translationcited by 0
  4. 2024Optimizing the Training Schedule of Multilingual NMT using Reinforcement Learningcited by 0