Research map / Continual learning / Online and replay-based
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CML instead of MLLRE | Continual relation extraction · Continual-FewRel, across task orders | Acc_a: 73.8 -> 75.0 | Helped |
| Use CML instead of MLLRE | Continual relation extraction · Continual-FewRel, across task orders | Acc_w: 58.9 -> 59.6 | Helped |
| Use CML instead of MLLRE | Continual relation extraction · Continual-FewRel, across task orders | Acc_a standard deviation across task positions: 1.69 -> 0.91 | Helped |
| Use CML instead of MLLRE | Continual relation extraction · Continual-FewRel, across task orders | Acc_w standard deviation across task positions: 2.17 -> 2.83 | Hurt |
| Use curriculum-meta learning instead of EMAR | Continual relation extraction · Continual-FewRel, across task orders | Acc_a: 73.8 -> 75.0 | Helped |
| Use curriculum-meta learning instead of EMAR | Continual relation extraction · Continual-FewRel, across task orders | Acc_w: 57.2 -> 59.6 | Helped |
Closest papers by meaning
- Continual Relation Extraction via Sequential Multi-Task Learning (AAAI 2024 · similarity 0.89)
- Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective (AAAI 2025 · similarity 0.87)
- Few-Shot, No Problem: Descriptive Continual Relation Extraction (AAAI 2025 · similarity 0.86)
- HOP to the Next Tasks and Domains for Continual Learning in NLP (AAAI 2024 · similarity 0.84)
- Learn more, but bother less: parameter efficient continual learning (NeurIPS 2024 · similarity 0.84)
- Learning to Continually Learn with the Bayesian Principle (ICML 2024 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.