Papers with DCL
Dual Contrastive Learning Framework for Incremental Text Classification (2023.findings-emnlp)
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| Challenge: | In incremental learning, large models learn and refresh knowledge continuously . many approaches have been proposed to preserve knowledge from previous tasks while learning new concepts in online NLP applications. |
| Approach: | They propose a dual contrastive learning framework that fosters transferability across different tasks . they use global contrastive and task-specific learning to promote a generalized embedding space . |
| Outcome: | The proposed framework outperforms the current state-of-the-art methods on text datasets. |
An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling (2025.acl-short)
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| Challenge: | Existing approaches to enhance sequence labeling models require data heterogeneity and additional modules. |
| Approach: | They propose a dual-stage curriculum learning framework specifically designed for sequence labeling tasks. |
| Outcome: | The proposed model improves training and accelerates training, mitigating the slow training issue of complex models. |
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
| Outcome: | The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. |