# Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection

arXiv: https://arxiv.org/abs/2204.07275 · HTML: https://mlsanity.com/paper/2204.07275/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 54 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021)
- [Episodic Memory in Lifelong Language Learning](https://mlsanity.com/paper/1906.01076/) (2019)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [Continual Lifelong Learning in Natural Language Processing: A Survey](https://mlsanity.com/paper/2012.09823/) (2020)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5](https://mlsanity.com/paper/2110.07298/) (2021)
- [Continual Learning in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2012.15504/) (2020)
- [Sentence Embedding Alignment for Lifelong Relation Extraction](https://mlsanity.com/paper/1903.02588/) (2019)
- [Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.03271/) (2021)
- [Continual Prompt Tuning for Dialog State Tracking](https://mlsanity.com/paper/2203.06654/) (2022)
- [Lifelong Language Knowledge Distillation](https://mlsanity.com/paper/2010.02123/) (2020)
- [CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.02714/) (2021)
- [Consistent Representation Learning for Continual Relation Extraction](https://mlsanity.com/paper/2203.02721/) (2022)
- [Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding](https://mlsanity.com/paper/2110.07476/) (2021)
- [The Art of Prompting: Event Detection based on Type Specific Prompts](https://mlsanity.com/paper/2204.07241/) (2022)

## Built on by (most cited first, 23 of 23)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning](https://mlsanity.com/paper/2207.12819/) (2022)
- [Continual Learning with Pre-Trained Models: A Survey](https://mlsanity.com/paper/2401.16386/) (2024)
- [Towards Lifelong Learning of Large Language Models: A Survey](https://mlsanity.com/paper/2406.06391/) (2024)
- [Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics](https://mlsanity.com/paper/2505.00675/) (2025)
- [X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects](https://mlsanity.com/paper/2311.08788/) (2023)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [On the Evolution of Knowledge Graphs: A Survey and Perspective](https://mlsanity.com/paper/2310.04835/) (2023)
- [Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction](https://mlsanity.com/paper/2305.16559/) (2023)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [An Investigation of the Combination of Rehearsal and Knowledge Distillation in Continual Learning for Spoken Language Understanding](https://mlsanity.com/paper/2211.08161/) (2022)
- [Continual Event Extraction with Semantic Confusion Rectification](https://mlsanity.com/paper/2310.15470/) (2023)
- [Schema-adaptable Knowledge Graph Construction](https://mlsanity.com/paper/2305.08703/) (2023)
- [Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas](https://mlsanity.com/paper/2601.08901/) (2026)
- [Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2305.13899/) (2023)
- [Continual Few-shot Event Detection via Hierarchical Augmentation Networks](https://mlsanity.com/paper/2403.17733/) (2024)
- [Lifelong Event Detection with Embedding Space Separation and Compaction](https://mlsanity.com/paper/2404.02507/) (2024)
- [Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need](https://mlsanity.com/paper/2406.03216/) (2024)
- [Lifelong Event Detection via Optimal Transport](https://mlsanity.com/paper/2410.08905/) (2024)
- [Double Mixture: Towards Continual Event Detection from Speech](https://mlsanity.com/paper/2404.13289/) (2024)
- [Class-Incremental Few-Shot Event Detection](https://mlsanity.com/paper/2404.01767/) (2024)
- [LEAF: A Robust Expert-Based Framework for Few-Shot Continual Event Detection](https://mlsanity.com/paper/2509.24547/) (2025)
- [The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence](https://mlsanity.com/paper/2605.02106/) (2026)

## Abstract (arXiv)

Lifelong event detection aims to incrementally update a model with new event types and data while retaining the capability on previously learned old types. One critical challenge is that the model would catastrophically forget old types when continually trained on new data. In this paper, we introduce Episodic Memory Prompts (EMP) to explicitly preserve the learned task-specific knowledge. Our method adopts continuous prompt for each task and they are optimized to instruct the model prediction and learn event-specific representation. The EMPs learned in previous tasks are carried along with the model in subsequent tasks, and can serve as a memory module that keeps the old knowledge and transferring to new tasks. Experiment results demonstrate the effectiveness of our method. Furthermore, we also conduct a comprehensive analysis of the new and old event types in lifelong learning.