Continual Learning and Adaptation in NLP

Continual learning and adaptation are essential capabilities for Natural Language Processing (NLP) systems to maintain relevanceand performance over time.This paper provides a comprehensive review of the current state-of-theart techniques, challenges, and applications related to continual learning and adaptation in NLP.We discuss various methodologies employed for continual learning, including incremental learning, transfer learning, and meta-learning, highlighting their strengths and limitations.Additionally, we examine the impact of continual learning on diverse NLP tasks such as sentiment analysis, machine translation, question answering, and language modelling.Furthermore, we identify key research directions and open challenges in this domain, paving the way for future advancements incontinual learning for NLP.