Multi-task Learning in NLP - Benefits and Challenges: Exploring the benefits and challenges of multi-task learning in NLP for training models to perform multiple related tasks simultaneously

Abstract

Multi-task learning (MTL) has emerged as a promising approach in natural language processing (NLP) for training models to perform multiple related tasks simultaneously. This paper explores the benefits and challenges of MTL in NLP, focusing on its applications, advantages, and potential limitations. We begin by providing an overview of MTL and its principles in NLP. We then discuss the benefits of MTL, including improved generalization, enhanced efficiency in model training, and the ability to leverage transfer learning. Additionally, we highlight the challenges associated with MTL in NLP, such as task interference, task misalignment, and the need for careful task selection and model architecture design. Finally, we present future research directions and conclude with a discussion on the potential impact of MTL on the field of NLP.