HOP to the Next Tasks and Domains for Continual Learning in NLP
This addresses the challenge of avoiding forgetting while transferring knowledge in NLP continual learning, but it is incremental as it builds on existing adapter-based approaches.
The paper tackles the problem of continual learning across multiple NLP tasks and domains in a unified framework, introducing the HOP method which uses adapters, high-order moments, and auxiliary heads, and demonstrates effectiveness through experiments on 4 NLP applications, 5 benchmarks, and 2 CL setups.
Continual Learning (CL) aims to learn a sequence of problems (i.e., tasks and domains) by transferring knowledge acquired on previous problems, whilst avoiding forgetting of past ones. Different from previous approaches which focused on CL for one NLP task or domain in a specific use-case, in this paper, we address a more general CL setting to learn from a sequence of problems in a unique framework. Our method, HOP, permits to hop across tasks and domains by addressing the CL problem along three directions: (i) we employ a set of adapters to generalize a large pre-trained model to unseen problems, (ii) we compute high-order moments over the distribution of embedded representations to distinguish independent and correlated statistics across different tasks and domains, (iii) we process this enriched information with auxiliary heads specialized for each end problem. Extensive experimental campaign on 4 NLP applications, 5 benchmarks and 2 CL setups demonstrates the effectiveness of our HOP.