A Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems

A Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems explores This research empirically compares catastrophic forgetting mitigation strategies for continual intent classification, identifying replay-based methods as crucial for robust adaptation in NLP systems.. Commercial viability score: 5/10 in Continual Learning for NLP.