SharpSeq: Empowering Continual Event Detection through Sharpness-Aware Sequential-task Learning (2024.naacl-long)
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| Challenge: | Existing methods for continual event detection suffer from catastrophic forgetting . a novel continual learning paradigm leveraging sharpness-aware minimization is needed . |
| Approach: | They propose a continual learning paradigm that leverages sharpness-aware minimization and a generative model to balance training data distribution. |
| Outcome: | The proposed approach outperforms existing methods on real-world datasets. |
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| Challenge: | Existing methods for knowledge graph completion are incomplete and can lead to errors . retraining the model with the entire updated TKG can mitigate forgetting but is computationally burdensome. |
| Approach: | They propose a temporal regularization framework that allows repurposing of parameters . they propose 'clustering-based experience replay' that reinforces the past knowledge . |
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Lifelong Event Detection via Optimal Transport (2024.emnlp-main)
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| Challenge: | Continual event detection (CED) is a challenging task due to catastrophic forgetting, where learning new tasks hampers performance on previous ones. |
| Approach: | They propose a method that leverages optimal transport principles to align the optimization of a classification module with the intrinsic nature of each class, as defined by their pre-trained language modeling. |
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Continual Few-shot Event Detection via Hierarchical Augmentation Networks (2024.lrec-main)
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| Challenge: | Existing methods for continual few-shot event detection use labeled data, but in real-world applications, new event types emerge continually. |
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Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning (2022.coling-1)
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| Challenge: | Existing rumor detection models assume the same training and testing distributions and can not cope with the continuously changing social network environment. |
| Approach: | They propose a Continual Prompt-Tuning RD framework which avoids catastrophic forgetting of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. |
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TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)
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| Challenge: | Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting. |
| Approach: | They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory. |
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Efficient Prompting for Continual Adaptation to Missing Modalities (2025.naacl-long)
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| Challenge: | Existing methods combine various missing cases to train recovery modules or align multimodal features, resulting in suboptimal performance, high computational costs, and catastrophic forgetting. |
| Approach: | They propose a continual multimodal missing modality task that uses prompts to learn modalities . existing methods often aggregate various missing cases to train recovery modules . authors conduct extensive experiments on three public datasets . |
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AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting (2025.findings-acl)
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| Challenge: | Keyword spotting (KWS) is a useful mechanism to identify spoken commands in voice-enabled systems, but catastrophic forgetting is causing models to lose their ability to recognize earlier keywords. |
| Approach: | They propose an exemplar-free method that updates model parameters without revisiting earlier data. |
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Continual Event Extraction with Semantic Confusion Rectification (2023.emnlp-main)
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| Challenge: | Existing studies focus on continual event extraction to extract incessantly emerging information . the semantic confusion on event types stems from the annotations of the same text being updated over time . |
| Approach: | They propose a continual event extraction model with semantic confusion rectification to reduce semantic confusion. |
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SEE: Continual Fine-tuning with Sequential Ensemble of Experts (2025.findings-acl)
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| Challenge: | Continual fine-tuning of large language models suffers from catastrophic forgetting . some approaches use routers to assign tasks to experts, but continual learning often requires retraining . |
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Continual Prompt Tuning for Dialog State Tracking (2022.acl-long)
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| Challenge: | Existing methods to train a model on a sequence of tasks are not efficient enough to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient framework that prevents forgetting and enables knowledge transfer between tasks by learning and freezing a pre-trained model. |
| Outcome: | The proposed framework avoids forgetting and enables knowledge transfer between tasks. |