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Introduction:

Multi-Task Learning (MTL) is a machine learning technique where a model is trained to perform multiple tasks simultaneously, with the aim of improving performance on each individual task. In recent years, MTL has shown promising results in the field of Natural Language Processing (NLP), where tasks such as text classification, sentiment analysis, named entity recognition, and machine translation can benefit from sharing information across tasks. This thesis aims to explore the application of MTL in NLP and investigate its effectiveness in improving the performance of various NLP tasks.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Research Motivation
1.3 Research Objectives
1.4 Research Questions
1.5 Scope of Study
1.6 Limitations of Study

Chapter 2: Literature Review
2.1 Introduction to Multi-Task Learning
2.2 Applications of Multi-Task Learning in NLP
2.3 Previous Studies on MTL in NLP
2.4 Challenges and Opportunities in MTL for NLP

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Model Architecture
3.3 Training Procedure
3.4 Evaluation Metrics
3.5 Baseline Models

Chapter 4: Discussion of Findings
4.1 Performance Comparison of MTL and Single-Task Learning
4.2 Analysis of Task Interference
4.3 Impact of Task Relatedness on MTL Performance
4.4 Model Interpretation and Error Analysis

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for NLP Research
5.4 Future Directions for Research in MTL for NLP

Thesis Overview:

Multi-Task Learning (MTL) is a powerful machine learning technique that has gained traction in the field of Natural Language Processing (NLP). By leveraging the shared information across multiple tasks, MTL aims to improve the performance of individual tasks and enhance the overall learning capabilities of a model. This thesis explores the application of MTL in NLP and investigates its effectiveness in improving the performance of various NLP tasks such as text classification, sentiment analysis, and named entity recognition. The study will involve a comprehensive literature review to understand the current state of MTL in NLP, followed by a detailed analysis of different MTL architectures and training procedures. The research methodology will include data collection, model development, and evaluation metrics to compare the performance of MTL against single-task learning approaches. The findings from the study will provide insights into the effectiveness of MTL for NLP tasks, the impact of task interference, and the importance of task relatedness in MTL performance. The conclusion will summarize the key findings, contributions of the study, and implications for future research in MTL for NLP.

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