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Instructions for Your Report Submission

Your report should adhere to the following guidelines to ensure clarity and consistency:

1. Length: The report must not exceed 10 pages.

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1. While additional materials can be included in appendices

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1. , the

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1. main

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1. report

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1. should be self-

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1. 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.

2. Submission: The final report must be uploaded to the main branch of your

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Your report needs to have the following structure, and should comply with the maximum page limit indications for each section:

Title

Intro

How does your conversational agent work?

Intent and Slot Classifier - performance, extensions to model.

Exclusion - how, testing, comparison with and without exclusion models accuracy

Extensions to bot (not including hpo and model updates)

Pilot User Study

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1. GitHub repository. Name the file Report.md.

2. Markdown Format: Use Markdown (.md format) 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.

3. Updating Your Report on GitHub:

   • Navigate to the Report.md file 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:

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• Keep it concise but reflective.

• Focus on high-level takeaways and actionable insights for future work.

Title

Add a title. Add your group number, student names, emails, and student numbers right below the title.

Introduction (max 1 page)

Briefly introduce your conversational recipe recommendation agent project, and describe the main goals you as a team have set yourselves to achieve.

How does your conversational agent work? (max 3 pages)

Think of this section as providing a kind of quick-start manual to a user who knows little about conversational agents (a friend or relative without any expertise in AI, for example). After reading this section, anyone should have a clear idea of what they can do with your agent. To this end, provide a functional specification of your agent, describing its main capabilities and the variety of conversational interactions a user can have with your agent. Mention all features or skills of your agent, so we can use that to test these features and skills based on what you write about how they work.

Design choices and rationale (max 3 pages)

In this section, you should describe which design choices you have made while implementing your agent. Design choices should include the more important choices that you made for developing your Dialogflow agent (NLU), for your MARBEL Dialog manager (patterns, responses, recipe database logic), and for what your agent displays while talking (visuals, webpages). You should also describe which extensions you choose to implement. Make sure to not only describe these design choices, but also motivate why you made these choices.

Test results and discussion (max 4 pages)

In this section, you should talk about the findings of testing your own agent. What did you find? There are two sets of data that you should discuss: 1. The data your team collected while developing your conversational agent during [TBU]Pipeline Testing; 2. The data your team collected in the [TBU]Pilot User Study (where fellow students tested your agent).

You should briefly present and analyze the results from both datasets. What were the main findings? What does the data show about how well your agent performed? What issues did they uncover? What does the data tell about your Dialogflow agent, about your Dialog manager, and about the pages you developed? What kind of results did you obtain from your pilot user study? Present both quantitative (numbers, figures, surveys) and qualitative (observations, feedback) data.

You should next try to explain, interpret and discuss the data. What insights and lessons learned can you draw from the data you collected yourselves and from the pilot user study? What does the data say about your design choices and the extensions that you implemented? Can you say anything about how these impacted the performance of your agent? What does the data tell about the effectiveness, efficiency, robustness of your agent, and what users (from your pilot user study) think about your agent (satisfaction).

Conclusion (max 1 page)

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