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**Introduction**

Deep learning models have achieved remarkable success in various natural language processing tasks, including natural language inference (NLI). NLI, also known as textual entailment, aims to determine the logical relationship between a premise and a hypothesis. However, the black-box nature of deep learning models raises concerns about their lack of interpretability and explainability. In the context of NLI, explainable deep learning techniques are crucial for ensuring transparency and trust in model predictions. This thesis explores the application of explainable deep learning for natural language inference, with a focus on enhancing the interpretability of deep learning models.

**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 Natural Language Inference
2.2 Deep Learning Models for NLI
2.3 Explainable AI in Natural Language Processing
2.4 Interpretable NLI Models
2.5 Evaluation Metrics for Explainable NLI Models
2.6 Challenges in Explainable Deep Learning for NLI
2.7 Interpretability vs. Performance Trade-off
2.8 Existing Approaches in Explainable NLI
2.9 Critique of Current State-of-the-Art
2.10 Future Directions in Explainable NLI Research

**Chapter 3: Research Methodology**
3.1 Introduction to Research Methodology
3.2 Data Collection and Preprocessing
3.3 Model Architecture Selection
3.4 Explainable AI Techniques Integration
3.5 Evaluation Framework Design
3.6 Experimental Setup
3.7 Performance Metrics Selection
3.8 Ethical Considerations in Research
3.9 Limitations of the Methodology

**Chapter 4: Discussion of Findings**
4.1 Performance Analysis of Explainable NLI Models
4.2 Comparison with Baseline Deep Learning Models
4.3 Interpretability of Model Predictions
4.4 Case Studies and Explanations
4.5 Impact of Explainable AI on User Trust
4.6 Discussion on Ethical Implications
4.7 Generalizability of Findings
4.8 Recommendations for Future Research

**Chapter 5: Conclusion and Summary**
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practitioners
5.4 Relevance to the Research Objectives
5.5 Limitations of the Study
5.6 Concluding Remarks
5.7 Future Research Directions

**Thesis Overview on Explainable Deep Learning for Natural Language Inference**

Deep learning models have shown significant promise in natural language inference tasks, but their lack of interpretability raises concerns about their trustworthiness. This thesis focuses on the application of explainable deep learning techniques to enhance the interpretability of NLI models. The literature review explores existing approaches in explainable AI for NLI and identifies gaps in the current state of the art. The research methodology outlines the data collection, model selection, and evaluation framework for studying explainable NLI models. The discussion of findings analyzes the performance and interpretability of the proposed models, along with insights on user trust and ethical considerations. The conclusion summarizes the key findings, contributions, limitations, and future research directions in the field of explainable deep learning for natural language inference.

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