Multitask learning techniques for natural language processing

Multitask learning techniques for natural language processing

作者：禅与计算机程序设计艺术

1.简介

Multitask learning is a machine learning technique that allows multiple related tasks to be learned simultaneously from the same data and then used together for better performance in each task. In recent years, multitask learning has emerged as an important approach towards improving the overall accuracy of modern NLP models while reducing computational costs. This paper presents a comprehensive review of multi-task learning techniques for natural language processing tasks. We start by describing various types of multitask learning approaches such as transfer learning, attention mechanisms, metalearning, hybrid methods, and self-supervised learning. Then we explore different algorithms such as neural networks, boosting, and feature fusion strategies for incorporating multiple tasks into a single model. Finally, we discuss factors like regularization, ensemble learning, and hyperparameter tuning to improve the performance o