Instructions for Your Report Submission
Your report should adhere to the following guidelines to ensure clarity and consistency:
Length: The report must not exceed 10 pages. While additional materials can be included in appendices, the main report should be self-contained and provide all necessary information for evaluation without requiring reference to the appendices. Markdown does not have pages necessarily. As a rule of thumb, one could say a page is about 500 words.
Submission: The final report must be uploaded to the main branch of your GitHub repository. Name the file Report.md.
Markdown Format: Use Markdown (
.mdformat) for your report. Markdown is a simple text formatting language widely used for documentation and README files. It allows for clean, structured text that can be easily viewed and edited.Updating Your Report on GitHub:
Navigate to the
Report.mdfile in your repository.Click the pencil icon to edit the file.
Make your changes, add a descriptive commit message, and click Commit changes to save.
Report Structure
Your report must follow the structure outlined below, with clear adherence to the page limits for each section.
1. Title (0.25 page)
Content:
Write a concise and descriptive title for your report.
Below the title, list your:
Group number
Student names
Emails
Student numbers
Notes:
Make the title eye-catching and informative to immediately communicate the essence of your project
2. Introduction (0.75 page)
Content:
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.
Tips:
Set a positive tone for the report and provide context for why conversational agents are valuable in recipe recommendations. Mention briefly the importance of personalization (e.g., excluding allergens, adapting to dietary preferences).
3. Your Pipeline: How Does Your Conversational Agent Work? (2 pages)
Content:
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.
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.
Tips:
Use diagrams or flowcharts to visually illustrate the pipeline. Focus on making it understandable to both technical and non-technical audiences.
4. Intent and Slot Classifier (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).
Extensions to the Model:
Mention improvements like:
Use of pre-trained models (e.g., BERT or GPT-based embeddings).
Hyperparameter tuning and architectural modifications.
5. Exclusion (2 pages)
Content:
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.
Tips:
Use examples and data to illustrate the effectiveness of the exclusion approach.
Be critical and discuss what could be improved.
6. Extensions to the Bot (1 page)
Content:
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.
Tips:
Highlight how these extensions make the bot stand out beyond baseline requirements.
7. Pilot User Study (1 page)
Content:
Setup:
Who were the users?
What tasks were they asked to perform?
Methodology for data collection (e.g., surveys, observation, interaction logs).
Results:
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 charts or graphs to present quantitative findings.
Include excerpts from user feedback to illustrate qualitative insights.
8. Conclusion (1 page)
Content:
Summarize the main outcomes of the project.
Reflect on the process:
What went well (e.g., teamwork, innovative solutions)?
What could be improved (e.g., time management, data quality)?
Suggest future improvements or extensions.
Tips:
Keep it concise but reflective.
Focus on high-level takeaways and actionable insights for future work.