Fundamental knowledge - Natural Language Understanding with Microsoft Copilot Studio
A fundamental component of Conversational AI is Natural Language Understanding (NLU). As human beings, when we talk to another person, we ask questions in our own language using words we know. A key component of conversational experiences for a user is the ability to ask questions naturally, while using vocabulary they understand, not some special language or syntax that has to be learned.
When a question is asked by a person, the person who is asked the question understands the intent from the question and acts accordingly. This discovered or assumed intent might be following up with a sentence, comment or even another question. What can make these interactions complex is matching the intent to the correct intention of the original user asking the question. This is understood using NLU technology, Natural Language Understanding, so a Conversational AI application can understand the natural language human beings use, recognize intents from questions, and then acts accordingly.
In traditional Conversational AI Development, developers have had to create, train, test and deploy a separate NLU layer with their logic and UI layers together as well as additional layers and integration around specific follow-up behavior. In Microsoft Copilot Studio, our aim is to democratize Conversational AI and make it easier for organizations to get started, manage, deploy and iterate on their agents by providing both low and pro code authoring experiences and capabilities. Copilot Studio comes straight out of the box with both Generative AI features, such as the Generative Answers, which you learned about in the previous task, and also its own pretrained Natural Language Understanding (NLU) capabilities. This means a maker doesn't need to use or provision their own NLU service and can get started straight away building authored topics and train their agent simply by entering Trigger Phrases within a topic. Additionally, standard and custom entities can be detected on intent recognition or within an ongoing conversation by extracting the subject of a user's sentence being stored and used as a variable.
Additionally, when creating conversational experiences and more than one authored topic in Copilot Studio, makers need to ensure they limit their topic overlap to avoid disambiguation scenarios that can occur when an NLU has two or more high ranking intents, which have been matched from the user's question. This means the NLU has to confirm with the user who asked the question what the intent was between those topics that has been matched (Did You Mean X, or Y?). Microsoft Copilot Studio has features including Topic Overlap Detection to help Agent Makers along the journey of agent building and the ability to turn off topics from showing up in the Did You Mean experience.
To learn more about Microsoft Copilot Studio NLU, including more detail on the underlying model and Topic Overlap capabilities, see Enable advanced AI features in Microsoft Copilot Studio.