Business Context
TechWave, a leading provider of AI-driven customer support solutions, aims to enhance its chatbot's ability to understand and respond to customer inquiries more effectively. By integrating attention mechanisms into their neural network models, they hope to significantly improve the accuracy of responses in real-time conversation scenarios.
Dataset
| Feature Group | Count | Examples |
|---|
| Text data | 100K | customer_queries, response_text |
| Metadata | 5 | query_length, response_time, sentiment_score |
- Size: 100K customer queries and responses
- Target: Categorical — response classification (e.g., 'help', 'complaint', 'feedback')
- Class balance: Balanced, with approximately 33% for each category
- Missing data: Minimal, with less than 2% missing values in metadata features
Requirements
- Implement a neural network with an attention mechanism to classify customer queries.
- Achieve at least 85% accuracy on the validation set.
- Provide a detailed explanation of how attention improves model performance.
- Evaluate model performance using precision, recall, and F1-score.
- Discuss potential trade-offs of using attention mechanisms in production.
Constraints
- The model must be capable of processing input in real-time, with a maximum inference latency of 200ms per query.
- The architecture should be scalable to handle increasing query volumes as TechWave expands its customer base.
