# Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation

arXiv: https://arxiv.org/abs/1906.01130 · HTML: https://mlsanity.com/paper/1906.01130/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 20 of 20)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones](https://mlsanity.com/paper/2211.09703/) (2022)
- [Prevent the Language Model from being Overconfident in Neural Machine Translation](https://mlsanity.com/paper/2105.11098/) (2021)
- [Coaching a Teachable Student](https://mlsanity.com/paper/2306.10014/) (2023)
- [EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training](https://mlsanity.com/paper/2405.08768/) (2024)
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- [M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2310.14605/) (2023)
- [Token-wise Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/2103.11088/) (2021)
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- [Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2501.00334/) (2024)
- [Secoco: Self-Correcting Encoding for Neural Machine Translation](https://mlsanity.com/paper/2108.12137/) (2021)
- [Accelerating the Training of Video Super-Resolution Models](https://mlsanity.com/paper/2205.05069/) (2022)
- [ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy](https://mlsanity.com/paper/2308.06450/) (2023)
- [Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text](https://mlsanity.com/paper/2311.06440/) (2023)
- [Curriculum for Crowd Counting -- Is it Worthy?](https://mlsanity.com/paper/2401.07586/) (2024)
- [Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2309.02640/) (2023)
- [A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2412.08489/) (2024)
- [Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy](https://mlsanity.com/paper/2607.18984/) (2026)

## Abstract (arXiv)

Noise and domain are important aspects of data quality for neural machine translation. Existing research focus separately on domain-data selection, clean-data selection, or their static combination, leaving the dynamic interaction across them not explicitly examined. This paper introduces a "co-curricular learning" method to compose dynamic domain-data selection with dynamic clean-data selection, for transfer learning across both capabilities. We apply an EM-style optimization procedure to further refine the "co-curriculum". Experiment results and analysis with two domains demonstrate the effectiveness of the method and the properties of data scheduled by the co-curriculum.