Content Comparison

Reflection Questions:

• Understanding Metrics

  • What does the intent accuracy score tell you about your model's ability to understand user inputs?

  • Why is the weighted F1-score important for evaluating slot classification? How does it provide a balanced view of the model's performance?

• Model Strengths and Weaknesses

  • Which intents or slots had the highest precision, recall, or F1-score? What does this indicate about the model's strengths?

  • Which intents or slots had the lowest scores? How could you address these weaknesses in future training or data collection?

• Improving the Model

  • If the intent accuracy is lower than expected, what steps would you take to improve it?

  • What actions could you take to improve slot tagging for low-performing slot types?

• Real-World Application

  • Based on the evaluation results, would you feel confident deploying this model for a real-world conversational agent? Why or why not?

  • What additional metrics or analyses might you consider before deployment?

• Automated Testing

  • How could the intent accuracy and slot weighted F1-score thresholds be used to determine whether a model is acceptable for submission?

  • Why is it beneficial to have automated testing in place for performance evaluation?

• Reflection on the Process

  • What did you learn from this evaluation process about your model’s capabilities and limitations?

  • How might these insights influence your approach to future iterations of the model?