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Introduction

Zero-shot learning (ZSL) is a challenging task in the field of artificial intelligence and machine learning, where the goal is to develop models that can answer questions on unseen topics without any prior training data. This is particularly important in open-domain question answering, where the questions can come from any domain or topic, making it impossible to train a model on every possible topic.

The ability to perform zero-shot learning for open-domain question answering has the potential to revolutionize the way we interact with machines and access information. By building models that can generalize to unseen topics, we can enable machines to provide more accurate and informative answers to a wider range of questions.

In this thesis, we will explore the challenges and opportunities of zero-shot learning for open-domain question answering. We will review the existing literature in this field, develop a research methodology to address the research questions, analyze the findings, and draw conclusions on the feasibility and effectiveness of zero-shot learning for open-domain question answering.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Zero-shot Learning
2.2 Zero-shot Learning for Question Answering
2.3 Open-domain Question Answering
2.4 Challenges of Zero-shot Learning
2.5 Approaches to Zero-shot Learning
2.6 Evaluation Metrics in Question Answering
2.7 Existing Models for Zero-shot Learning
2.8 Related Work in Zero-shot Learning
2.9 Limitations of Existing Approaches
2.10 Future Directions in Zero-shot Learning

Chapter 3: Research Methodology
3.1 Introduction
3.2 Data Collection
3.3 Model Selection
3.4 Evaluation Criteria
3.5 Experimental Setup
3.6 Performance Metrics
3.7 Data Analysis Techniques
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Analysis of Experimental Results
4.3 Comparison of Different Approaches
4.4 Interpretation of Results
4.5 Implications of Findings
4.6 Limitations of the Study
4.7 Recommendations for Future Research
4.8 Practical Applications of Zero-shot Learning in Question Answering

Chapter 5: Conclusion
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion

In this thesis, we aim to provide a comprehensive overview of the current state of zero-shot learning for open-domain question answering, identify the challenges and opportunities in this field, and propose novel approaches to improve the performance of question answering systems in unseen domains.

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