NLU

NLU is an acronym for natural-language understanding. It is a part of natural-language processing (NLP) Also called NLP, this process understands human speech or text and responds with human-like language. that deals specifically with machine reading comprehension. It is the part of Agent Builder that allows your Mpower Agent A virtual agent created with CXone Mpower Agent Builder that can handle voice or chat interactions. to understand what your consumers are saying in conversations so it can provide the most helpful response.

The NLU section of Agent Builder allows you to create and manage the configurations that build your bot's NLU capabilities. These are entities and intents. These concepts build your training data and make your Mpower Agent smarter.

Glossary

Concept	Definition	Example	What the Mpower Agent Does
Utterance	Anything a contact The person interacting with an agent, IVR, or bot in your contact center. says in an interaction The full conversation with an agent through a channel. For example, an interaction can be a voice call, email, chat, or social media conversation.. Sometimes called a message.	"I lost my password." "What is my balance?" "Are you a bot?"	The Mpower Agent uses Natural Language Understanding (NLU) to analyze each contact utterance to determine its meaning, or intent.
Intent	What the contact wants to communicate or accomplish. Every message the contact sends has an intent.	"I lost my password" has the intent of "reset password". "Hello" has the intent of "greeting".	The Mpower Agent analyzes a contact's message using NLU This process expands on Natural Language Processing (NLP) to make decisions or take action based on what it understands. to determine the intent. Once it knows that, it can respond with a message of its own. You configure the response you want your Mpower Agent to use for each intent.
Entity	A defined piece of information in a contact's message.	Person or product name, phone number, account number, location, and so on.	The Mpower Agent uses NLU to identify entities in a contact's message. Entities help the Mpower Agent understand what the contact's message means.
Slot	An entity extracted from a contact's message and saved for use in Mpower Agent responses. Similar to a variable.	Creating a slot for contact name lets the Mpower Agent use that name in responses during an interaction, making it more personal.	When configured to do so, the Mpower Agent extracts an entity from a contact message and saves it in a slot. You can have your Mpower Agent use this information later in the conversation.
Rule	Defines Mpower Agent responses to messages that don't change meaning with context.	Single-turn interactions with fixed responses: What are your hours? What is your address? Conversation building blocks: Greetings, good-byes, thank yous and transitions; simple yes/no questions; and acknowledgments. Mpower Agents come with default intents and rules for several of these, including greetings, handover The transfer of a contact from a virtual agent to a live agent. requests, and more. FAQs Insults and classic bot challenges	Rules are one of two ways you can configure how your Mpower Agent responds to an intent. Rules are useful for certain kinds of intents, but not all intents.
Story	Trains an Mpower Agent to handle an interaction based on message intent and conversational context.	In an interaction about a forgotten password, the Mpower Agent would respond to "How do I do that?" in one way. If the interaction were about creating a new account, the response would be quite different even though in both cases the contact is using the same words with the same intent—to get more information.	Stories are the second of two ways you can configure how your Mpower Agent responds to an intent. Stories teach the Mpower Agent how to use the context of the conversation to respond appropriately.
Mpower Agent Action	Anything an Mpower Agent says or does while handling an interaction.	In an interaction about a forgotten password, the Mpower Agent responds by sending the link to the password reset FAQ on the website. When a contact expresses frustration, such as "I don't understand! It's not working!!!" the Mpower Agent responds with "I'm sorry. Would you like me to transfer you to a human agent?" When the contact says yes, the Mpower Agent initiates the transfer.	Mpower Agent actions are the options you have when defining how you want your Mpower Agent to respond to each intent. They give you the flexibility to configure each response to achieve the outcome that meets the contact's needs.

Best Practices for NLU Training Data

Always opt for quality over quantity. It's okay to start with a small data set and build it over time as you gather more high-quality examples.
Use examples from real-world conversations. This ensures that the data you use is realistic. It comes from utterances What a contact says or types. real contacts have made.
Don't use tools that auto-generate data and claim to train your Mpower Agent faster. They often produce examples that don't reflect what contacts really say. They also can result in an Mpower Agent that loses its ability to generalize. Over time, the Mpower Agent reaches a point where it only recognizes phrases it's seen before.
Do not use the same training data for more than one intent. If you re-use training data, your Mpower Agent won't be able to reliably determine the intent The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish. in live interactions with contacts.
Remain flexible and willing to adjust intents and stories over time. As you review conversation data, you may discover that what you thought was two separate intents are really shades of a more general intent. Or you may find that an intent is too broad and you need to break it down into more specific intents.
Add new training examples only if they will help.
- Do not add new training examples that are very similar to existing examples. If the Mpower Agent correctly predicts the intent with a high confidence for one utterance, it doesn't help the Mpower Agent to add more examples that are very similar.
- Do add more training examples of utterances the Mpower Agent has previously predicted incorrectly or with low confidence.