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?
Click on the arrows to vote for the correct answer
A. B. C. D.D
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-baseThe correct answer is D. active learning.
Active learning is a machine learning technique that involves selecting the most informative examples for labeling by an expert. In the context of a QnA bot, active learning can be used to improve the relevance of the bot's responses over time by allowing users to provide feedback on the accuracy and helpfulness of the bot's responses.
When a user interacts with the bot, they can indicate whether the bot's response was helpful or not. This feedback is then used to update the knowledge base and improve the accuracy of future responses. Active learning can also be used to identify areas where the bot's knowledge is lacking, so that the knowledge base can be updated to better address those topics.
Key phrase extraction and sentiment analysis can also be useful in certain contexts, but they are not directly related to improving the relevance of responses from a QnA Maker knowledge base. Business logic is a programming technique used to define the rules and behavior of a system, but it does not directly involve machine learning or natural language processing.