Advancements and Challenges in Continual Learning for Natural Language Processing: Insights and Future Prospects

Asma Kharrat, Fadoua Drira, Franck Lebourgeois, Bertrand Kerautret

2024

Abstract

Deep learning-based Natural Language Processing (NLP) has advanced significantly over the past decades, in light of static learning’s remarkable performance across a range of text datasets. However, this method heavily relies on static surroundings and predefined datasets, making it difficult to manage ongoing data streams without losing track of previously acquired knowledge. Continual learning provides a more effective and adaptable framework. It tries to make it possible for machine learning models to learn from an ongoing data stream while maintaining their prior knowledge. In the context of NLP, continual learning presents unique challenges and opportunities due to its dynamic and diversity. In this paper, We shall provide a thorough analysis of CL’s most recent advancements in the NLP disciplines in which major challenges are illustrated. We also critically review the existing CL evaluation solutions and benchmarks in NLP. Finally, we present open issues that we consider need further investigations and our outlook on future research directions.

Paper Citation

in Harvard Style

Kharrat A., Drira F., Lebourgeois F. and Kerautret B. (2024). Advancements and Challenges in Continual Learning for Natural Language Processing: Insights and Future Prospects. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1255-1262. DOI: 10.5220/0012462400003636

in Bibtex Style

@conference{icaart24,
author={Asma Kharrat and Fadoua Drira and Franck Lebourgeois and Bertrand Kerautret},
title={Advancements and Challenges in Continual Learning for Natural Language Processing: Insights and Future Prospects},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1255-1262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012462400003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Advancements and Challenges in Continual Learning for Natural Language Processing: Insights and Future Prospects
SN - 978-989-758-680-4
AU - Kharrat A.
AU - Drira F.
AU - Lebourgeois F.
AU - Kerautret B.
PY - 2024
SP - 1255
EP - 1262
DO - 10.5220/0012462400003636
PB - SciTePress