Research on the Application of Natural Language Processing Based on Deep Learning in Multi-task Learning

This article comprehensively discusses the application of natural language processing (NLP) based on deep learning in multi-task learning (MTL). First, the article reviews the basic principles of deep learning, the development history of NLP, the concept of MTL and its application in various fields. Next, the article analyzes in detail the specific application cases of deep learning in NLP, such as language models, sentiment analysis and machine translation, and discusses the successful cases and method summaries of these technologies. In particular, this article focuses on the application of MTL in NLP, and experimentally verifies the advantages of MTL in performance improvement compared to the traditional single-task learning (STL) mode. The experimental part covers research methods and experimental design, data sets and experimental environment, experimental process and result analysis, as well as result discussion and theoretical significance. Finally, the article demonstrates the potential of MTL to improve model generalization capabilities in NLP tasks, and points out the limitations of the research and possible future research directions. The innovation of this study lies in the proposed new deep learning model structure, which is specially optimized for the application of MTL in NLP and its effectiveness is verified through experiments.

I. Introduction

With the rapid development of artificial intelligence and machine learning technology, natural language processing (NLP) has become a hot area of research. The goal of NLP is to enable computers to understand and process human language to achieve functions such as automatic translation, sentiment analysis, and intelligent question and answer. The introduction of deep learning has brought revolutionary changes to NLP, especially showing unprecedented capabilities in processing large-scale language data. However, with the increase in tasks and the complexity of data, the traditional single-task learning model has gradually revealed its limitations. Multi-task learning (MTL), as an effective learning strategy, improves learning efficiency and model generalization ability by learning related tasks at the same time [1].