Learn how to optimize your webchat bot’s responses by utilizing user feedback to enhance relevance over time. Explore the benefits and functionalities of key phrase extraction, sentiment analysis, business logic, and active learning. Elevate the performance of your bot by incorporating user feedback into its learning process and delivering more accurate and valuable responses.
Table of Contents
Question
You have a webchat bot that provides responses from a QnA Maker knowledge base. You need to ensure that the bot uses user feedback to improve the relevance of the responses over time. What should you use?
A. key phrase extraction
B. sentiment analysis
C. business logic
D. active learning
Answer
D. active learning
Explanation
The correct answer is D. active learning.
Active learning is a feature of QnA Maker that allows the bot to learn from user feedback and improve the relevance of the responses over time. Active learning works by identifying questions that the bot is not confident about and suggesting alternative answers from the knowledge base. The bot then asks the user to rate the answer or select a better one from the suggestions. The user’s feedback is then used to update the ranking algorithm and train the bot to provide more accurate answers in the future.
Key phrase extraction, sentiment analysis, and business logic are not directly related to improving the relevance of the responses over time. Key phrase extraction is a natural language processing technique that identifies the most important words or phrases in a text. Sentiment analysis is a natural language processing technique that determines the emotional tone or attitude of a text. Business logic is a set of rules or procedures that define how a bot should behave or respond to user requests.
References
Microsoft Docs > Azure > Cognitive Services >QnA Maker > Accept active learning suggested questions in the knowledge base
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