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

arXiv 1906.01130 ↗First posted 3 Jun 2019cs.CL cs.LGcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2019.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 6; 2024: 4; 2025: 0; 2026: 1.

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  1. 2021Curriculum Learning: A Surveycited by 257
  2. 2022EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbonescited by 42
  3. 2021Prevent the Language Model from being Overconfident in Neural Machine Translationcited by 34
  4. 2023Coaching a Teachable Studentcited by 17
  5. 2024EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Trainingcited by 16
  6. 2023Imitation Learning from Observation with Automatic Discount Schedulingcited by 10
  7. 2022Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning Strategycited by 7
  8. 2023M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysiscited by 6
  9. 2021Token-wise Curriculum Learning for Neural Machine Translationcited by 4
  10. 2021In-N-Out: Towards Good Initialization for Inpainting and Outpaintingcited by 4
  11. 2022CLIP: Train Faster with Less Datacited by 4
  12. 2024Loss-Aware Curriculum Learning for Chinese Grammatical Error Correctioncited by 3
8 more
  1. 2021Secoco: Self-Correcting Encoding for Neural Machine Translationcited by 2
  2. 2022Accelerating the Training of Video Super-Resolution Modelscited by 2
  3. 2023ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategycited by 1
  4. 2023Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textcited by 1
  5. 2024Curriculum for Crowd Counting -- Is it Worthy?cited by 1
  6. 2023Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translationcited by 0
  7. 2024A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysiscited by 0
  8. 2026Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomycited by 0