Summary of Hop to the Next Tasks and Domains For Continual Learning in Nlp, by Umberto Michieli et al.
HOP to the Next Tasks and Domains for Continual Learning in NLP
by Umberto Michieli, Mete Ozay
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper presents a novel approach to Continual Learning (CL), which enables machines to learn from a sequence of problems by transferring knowledge acquired on previous tasks while avoiding forgetting. Unlike previous methods that focused on a single task or domain, this framework addresses CL in a more general setting. The method, called HOP, involves three key components: adapter-based generalization to unseen problems, high-order moment computation to distinguish independent and correlated statistics across tasks and domains, and auxiliary heads for each end problem. Experimental results on 4 NLP applications, 5 benchmarks, and 2 CL setups demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way for machines to learn from many different problems. It’s called Continual Learning (CL). The goal is to help machines remember what they learned before, even as they move on to new challenges. This method, HOP, helps machines do just that by using special tools like adapters and high-order moments. These tools let the machine figure out how each problem is similar or different from others it has seen before. It’s tested on many real-world problems and shows great results. |
Keywords
* Artificial intelligence * Continual learning * Generalization * Nlp
