Content Comparison

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1. Dialogflow:

   • Used for initial intent and slot classification.

   • Allows rapid prototyping and testing of MARBEL agent functionalities.

2. Intent and Slot Classifier:

   • Replaces Dialogflow once operational.

   • Provides a robust, customizable, transformer-based solution.

3. MARBEL Agent:

   • Designed for structured, multi-turn dialogue interactions.

   • Handles recipe selection, filtering, and confirmation tasks.

4. Prolog Recipe Database:

   • Offers efficient recipe retrieval based on user preferences.

5. WHISPER:

   • Adds ASR and TTS functionalities for speech-based interaction.

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Development Workflow

The development workflow is divided into three structured phases to guide students through building and extending the NLU pipeline:

• Phase 1: Inclusion Filtering
  Students begin by implementing inclusion filters, such as "Find me a recipe with salt from Italy." This involves defining intents and slots, training the intent and slot classifier, and integrating it with the MARBEL agent to enable Prolog-based recipe retrieval.

• Phase 2: Exclusion Filtering
  The next step is extending the system to handle exclusion filters, like "I want a recipe without salt." Students update datasets, retrain the model to recognize exclusion intents and slots, and adapt MARBEL patterns to manage combined filtering scenarios.

• Phase 3: Custom Extensions
  Students then innovate by adding their own extensions, such as multi-turn dialogue, advanced filtering for dietary restrictions, or enhanced context handling. This phase emphasizes creativity and the practical application of NLU principles to real-world problems.

This phased workflow ensures an incremental learning process, fostering a comprehensive understanding of NLU systems and their integration into conversational agents.

How you separate work is up to your group.