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Introduction to the Project:
Define a conversational recipe recommendation agent and its purpose.
Introduce Task-Oriented Spoken Dialogue Systems (TOSDS), emphasizing their role in handling structured tasks like recipe recommendations.
Preliminaries:
Explain any key definitions, methodologies, or prior knowledge (e.g., intents, slots, NLU pipelines, or ontology design) that you used as foundational elements.
Goals:
State your team’s objectives clearly. Examples:
Build an agent capable of personalized recipe recommendations.
Etc.
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These should be concrete and specific goals to your version of the agent.
Etc.
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3.
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How Does Your Conversational Agent Work? (2 pages)
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Content:
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:
Provide a high-level description of the architecture, from user input to recipe output.
Mention the key components seen in the diagram above as described in [TBC]Preliminaries and Quiz Materials.
Functionality:
Highlight the agent’s primary use cases.
Conversational Flow:
Walk through a typical user interaction.
Explain how user queries are processed through intent recognition, slot filling, and database queries.
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Examples: Give us typical interactions that would work with your agent. Show us what your agent can do by giving examples that illustrate your agent's capabilities. Note: these examples should be useful for testing (we may try them out).
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4. Intent and Slot Classifier (1-2 pages)
Content:
Role of Intent and Slot Classifier:
Explain the importance of these components in identifying user intentions and extracting relevant information (e.g., cuisine type, dietary restrictions).
Training and Testing:
Describe the datasets used for training/testing.
Highlight any preprocessing techniques or augmentation strategies employed.
Performance Analysis:
Present metrics:
Accuracy, precision, recall, F1 score.
Use tables or confusion matrices to compare results across iterations.
Discuss challenges faced (e.g., ambiguous intents, overlapping slots).
Discuss the comparison of your results and the Intent and Slot Classifier Evaluation Thresholds
Model Improvements:
Mention improvements like:
Use of pre-trained models (e.g., BERT or GPT-based embeddings).
Hyperparameter tuning, training methodology augmentation, and architectural modifications.
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Implementation:
How exclusion works (e.g., excluding ingredients, cuisines, and mealTypes).
Approaches used: Describe the approach your team used to implement Exclusion into your model.
Tools/technologies: Integration of MARBEL, Prolog, Python, and ontology updates.
Pros and Cons:
Discuss the strengths and limitations of the exclusion mechanism you choose. What can your exclusion do, what can it not do?
Testing:
Comparison of inclusion-only vs. exclusion models.
Performance Analysis:
Accuracy with and without exclusion.
Trade-offs and impact on user satisfaction.
Comparison of inclusion-only vs. exclusion models.
Tips:
Use examples and data to illustrate the effectiveness of the exclusion approach.
Be critical and discuss what could be improved.
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6. Extensions to the
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Pipeline (1 page)
Content:
Summarize the work done related to Extensions.
Describe additional enhancements:
New functionalities, filters, capabilities added.
Improvements in user interaction and experience (e.g., better response generation, conversational adaptability).
Explain the motivation and impact of these extensions.
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Highlight how these extensions make the bot agent stand out beyond baseline requirements.
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Setup:
Who were the users?
What tasks were they asked to perform?
Methodology for data collection (e.g., surveys, observation, interaction logs).
Results from the Study:
Simple descriptive statistics gathered in the Pilot User Study
Quantitative data: Success rates, error rates, average response time, etc.
Qualitative data: User feedback, observations of user behavior.
Analysis:
Key findings: What worked well? What needs improvement?
Lessons learned and implications for future work.
Tips:
Use You do not need to use charts or graphs to present quantitative findings.
Include excerpts from user feedback to illustrate qualitative insights.
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