Content Comparison

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1. 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.

2. Preliminaries:

   • Explain any key definitions, methodologies, or prior knowledge (e.g., intents, slots, NLU pipelines, or ontology design) that you used as foundational elements.

3. Goals:

   • State your team’s objectives clearly. Examples:

     • Build an agent capable of personalized recipe recommendations.

     • Etc.

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1. • • 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:

1. Pipeline Overview:

   • 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.

2. Functionality:

   • Highlight the agent’s primary use cases.

3. 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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1. 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:

1. 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.

2. 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).

   Extensions to the Model

   • Discuss the comparison of your results and the Intent and Slot Classifier Evaluation Thresholds

3. 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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