Papers with curriculum learning framework
HuCurl: Human-induced Curriculum Discovery (2023.acl-long)
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| Challenge: | Existing curriculum learning frameworks can be used to discover effective curricula for NLP tasks based on prior knowledge about sample difficulty. |
| Approach: | They propose a framework for curriculum learning based on prior knowledge about sample difficulty. |
| Outcome: | The proposed framework outperforms existing curriculum learning approaches on several NLP tasks and can prune and weight samples for better learning. |
Competence-based Curriculum Learning for Neural Machine Translation (N19-1)
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| Challenge: | Existing NMT systems require specialized heuristics and large batch sizes. |
| Approach: | They propose a curriculum learning framework for NMT that reduces training time and costs . framework consists of a principled way of deciding which training samples are shown to the model . |
| Outcome: | The proposed framework can reduce training time and improve performance of recurrent neural network models and Transformers. |
MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce (2026.acl-industry)
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| Challenge: | Vision-language models struggle with noisy real-world images and multi-task requirements. |
| Approach: | They propose a curriculum learning framework that adapts vision-language models through three stages . MTIVE uses frozen base weights with stacked LoRA adapters for shared domain knowledge . |
| Outcome: | MTIVE outperforms open-source and proprietary baselines in standard and continual learning settings. |
Metaphor Detection with Context Enhancement and Curriculum Learning (2024.naacl-long)
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| Challenge: | Metaphor detection is a challenging task for natural language processing systems . previous work failed to adequately utilize internal and external semantic relationships . |
| Approach: | They propose a model that leverages the difference between literal and external meanings of words and sentences as the sentence external difference. |
| Outcome: | The proposed model achieves competitive performance across multiple datasets with improved convergence speed compared to other models. |
Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning (2023.findings-emnlp)
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| Challenge: | Non-compositional expressions are a classic ‘pain in the neck’ for NLP systems because of their non-composibility and limited data resources. |
| Approach: | They propose a dynamic curriculum learning framework which learns training examples from easy ones to harder ones but suffers from the forgetting problem. |
| Outcome: | The proposed framework improves on idiomatic expression generation and metaphor generation. |
Learning from Children: Improving Image-Caption Pretraining via Curriculum (2023.findings-acl)
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| Challenge: | Image-caption pretraining is a difficult problem as it requires multiple concepts (nouns) from captions to be aligned to multiple objects in images. |
| Approach: | They propose a curriculum learning framework that uses images to align multiple concepts to multiple objects in an image. |
| Outcome: | The proposed learning framework improves over pretraining from scratch, using a pretrained image or/and text encoder, low data regime etc. |