Implementing NLP: Episode 3
Introduction: Following our previous episodes, where we set up a structured database and refined our NLP model, this episode dives into establishing a seamless conversation flow between users and our AI.
Objective: With a robust NLP model capable of accurately interpreting multiple intentions, our focus shifts to creating fluid interactions between users and AI.
The Challenge of Conversation Management: Conversations are nuanced, requiring AI to understand and simulate natural interactions. This involves grounding, where the AI gradually comprehends discussion elements as they unfold.
Chatbots & Dialogue Systems: Effective conversation management necessitates understanding the context and continuity of interactions.
Approaches to Conversation Management: Two main approaches exist:
- NLP Tool Reliance: Using pre-built NLP tools for managing conversations.
- Custom Solutions: Developing tailored architectures to meet specific needs, which is our chosen approach.
The Choice of Custom Architecture: We opted for a custom solution to ensure flexibility and adaptability. This architecture includes:
- User Profile Dimension: Tailored responses based on user business domains and preferences.
- Conversation History Management: Facilitates rapid troubleshooting and model enhancement.
Simplified Architecture Overview: Each user message undergoes several steps within our architecture:
- NLP Analysis: The message is analyzed to identify relevant entities.
- Entity Normalization: Entities are converted into predefined values.
- Database Storage: Normalized attributes are stored in a database, serving as the AI’s memory for conversation continuity.
Concrete Conversation Flow Example:
- User Query: The user requests to buy Apple shares.
- NLP Interpretation: The AI interprets the query, considering known user preferences.
- Additional Information: The AI prompts the user for more details, such as the desired market.
- Market Selection: The AI presents a list of suitable markets, simplifying user interaction.
- Execution: The user provides the necessary details, and the AI executes the query.
Conclusion: This episode covers the intricacies of conversation flow management, emphasizing the importance of a custom architecture for enhancing NLP capabilities. Looking ahead, further enhancements like performance analysis tools and automated annotation features are anticipated.
Stay tuned for more updates!
For a detailed guide on advanced NLP implementation, refer to Terranoha’s Episode 3.
