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Briefly introduce the project: What is a conversational recipe recommendation agent? What are Task-Oriented Spoken Dialogue Systems?
Summarize Preliminaries: Describe what definitions and knowledge you utilized to complete this project?.
State the goals your team aimed to achieve.
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Ensure clarity and conciseness. This section sets the stage for the reader.
Provide some background on the significance of conversational agents in recipe recommendationrecommendations.
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3. Your Pipeline: How Does Your Conversational Agent Work? (2 pages)
Content:
Describe your pipeline, and give an overview.
Explain the overall functionality of your agent.
What problems does it solve?
What can users achieve by interacting with the agent?
Describe the conversational flow:
Main features (e.g., recipe suggestions, ingredient substitutions).
Supported interactions (e.g., asking for specific cuisines, excluding allergens).
Functional specification:
Technical summary of how the agent is designed (without getting overly detailed yet).
Tips:
Include examples of interactions (e.g., “User: Show me vegan recipes. Agent: Here are some vegan options”).
Keep it simple and user-focused.. How does it work?
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4. Intent and Slot Classifier (2 pages)
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Explain the role of the intent and slot classifier in the agent.
Summarize training and testing
Performance analysis:
Metrics: Accuracy, precision, recall, F1 score, confusion matrix.
Any improvements made, such as additional training data or , custom models, and improvements to the model or training procedure.
Discuss challenges:
Ambiguous intents or overlapping slots and how these were addressed.
Extensions to the model:
Example: Use of other pre-trained embeddings, models, hyperparameter tuning, additional or custom layers, or domain-specific tuning.
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