# Learning a Multi-Domain Curriculum for Neural Machine Translation

arXiv: https://arxiv.org/abs/1908.10940 · HTML: https://mlsanity.com/paper/1908.10940/

## Key facts

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

## Built on by (most cited first, 16 of 16)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [CLNode: Curriculum Learning for Node Classification](https://mlsanity.com/paper/2206.07258/) (2022)
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- [In-Context Demonstration Selection with Cross Entropy Difference](https://mlsanity.com/paper/2305.14726/) (2023)
- [IRGPT: Understanding Real-world Infrared Image with Bi-cross-modal Curriculum on Large-scale Benchmark](https://mlsanity.com/paper/2507.14449/) (2025)
- [Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2306.03511/) (2023)
- [Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR](https://mlsanity.com/paper/2606.25178/) (2026)
- [Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization](https://mlsanity.com/paper/2109.08569/) (2021)
- [Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task](https://mlsanity.com/paper/2409.14800/) (2024)
- [DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation](https://mlsanity.com/paper/2204.09259/) (2022)
- [BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition](https://mlsanity.com/paper/2308.08625/) (2023)
- [Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation](https://mlsanity.com/paper/2407.01126/) (2024)
- [HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks](https://mlsanity.com/paper/2409.14842/) (2024)
- [Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2309.02640/) (2023)
- [Optimizing the Training Schedule of Multilingual NMT using Reinforcement Learning](https://mlsanity.com/paper/2410.06118/) (2024)

## 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.