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Continual Learning and Adaptation in NLP

Year: 2025

Type: conference-paper

Abstract: 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.

DOI: 10.22161/ijaems.ncitsa.2025.43

Authors: Md. Rizwan Khan, Khushi Mittal, Rupal Todwal

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4412362157

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