Dialogue systems are a crucial component of modern AI applications. This page provides an overview of the key aspects of dialogue system design.

Key Components of Dialogue Systems

  1. User Input Processing

    • Natural Language Understanding (NLU): Converts user input into structured data.
    • Entity Recognition: Identifies and extracts key information from the input.
    • Intent Recognition: Determines the user's intention behind the input.

  2. Dialogue Management

    • Dialogue State Tracking: Keeps track of the context and state of the conversation.
    • Dialogue Policy: Determines the system's response based on the current state and user input.

  3. Response Generation

    • Template-based Responses: Uses predefined templates to generate responses.
    • Natural Language Generation (NLG): Generates human-like responses based on the dialogue state.

  4. Feedback Loop

    • User Feedback: Collects user feedback to improve the system.
    • Continuous Learning: Uses machine learning techniques to refine the dialogue system.

Resources

For more information on dialogue system design, you can refer to the following resources:

Dialogue System Architecture