Summary of Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System, By Chen Chen et al.
Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System
by Chen Chen, Ruizhe Li, Yuchen Hu, Yuanyuan Chen, Chengwei Qin, Qiang Zhang
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of continual learning (CL) in intelligent task-oriented dialogue systems (ToDs). CL allows these systems to adapt to changing user needs by acquiring new knowledge. However, current approaches often suffer from catastrophic forgetting, which causes performance degradation when faced with a long stream of new tasks. To overcome this issue, the authors propose HESIT, a method that uses hyper-gradient-based exemplar strategy for periodic retraining. HESIT selects influential exemplars for each task domain by analyzing the training data’s influence on the model’s optimization process. Experimental results show that HESIT effectively alleviates catastrophic forgetting and achieves state-of-the-art performance on the largest CL benchmark for ToDs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart computer program that can have conversations with humans. This program is called a dialogue system, and it’s really good at answering questions or helping us complete tasks. But what if this program needs to learn new things over time? That’s where the problem of “forgetting” comes in – the program might forget what it learned earlier because it’s trying to adapt to new information. The researchers in this paper developed a new way for dialogue systems to remember what they’ve learned, called HESIT. They tested it and found that it works really well, even better than previous methods! This could lead to more helpful and adaptable computer programs in the future. |
Keywords
» Artificial intelligence » Continual learning » Optimization
