Continual Learning and Adaptation in NLP

Continual learning and adaptation are essential capabilities for Natural Language Processing (NLP) systems to maintain relevance and performance over time.This paper provides a comprehensivereviewofthecurrentstate-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 examinetheimpactofcontinuallearningondiverse 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 in continual learning for NLP.