Continual Named Entity Recognition without Catastrophic Forgetting (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (CNER) is a burgeoning area of research . a new paradigm has ushered NER into a non-entity type at the current step t . |
| Approach: | They propose a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on ten CNER settings using three datasets. |
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| Challenge: | Existing methods for Named Entity Recognition (CNER) use knowledge distillation to retain old knowledge, but they are too expensive and fail to integrate with existing state-of-the-art models. |
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| Challenge: | Continual learning for named entity recognition (CL-NER) aims to enable models to continuously learn new entity types while retaining the ability to recognize previously learned ones. |
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| Challenge: | Existing methods for named entity recognition are based on pre-fixed entity types, resulting in catastrophic forgetting. |
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| Challenge: | Existing methods for Named Entity Recognition (NER) are not able to learn Other-Class in the same way as new entity types. |
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Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)
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| Challenge: | Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved. |
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| Challenge: | Named entity recognition (NER) is a task in natural language processing that aims at locating entity mentions in a given sentence and assigning them to certain types. |
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| Challenge: | Existing studies on pre-trained language models focus on task-incremental learning (TIL) but they perform poorly in a more challenging setting of class-incremental learning. |
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| Challenge: | Existing methods to label data and identify entities require large amounts of manually annotated texts for training supervised models. |
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Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models (C18-1)
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| Challenge: | Existing models for Named Entity Recognition (NER) are trained on data with the same NE label set, but they are not able to recognize previously unseen NE categories. |
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| Challenge: | Named Entity Recognition (NER) methods require a substantial quantity of high-quality annotation for training models. |
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