Train

After you published your bot, you want to keep training the NLP model with real user input. By doing so, your bot becomes smarter over time and can support more diverse expressions too. To do so, you can use the NLP Train tab to add real user expressions to your NLP model.

How the Train page works

All user messages are labeled by the NLP model. Each message gets a suggested intent and a confidence score so that you can evaluate these messages. The NLP also identifies possible entities and values.

Expressions not included

• Expressions from the Emulator window will not be included in the Train page.

• If two users use the exact same expression, it will only show up once in the Train page.

• If an expression from a user is an exact match with an expression already included in your model, it will not be included in the Train tab.

• In the Score column you will see the score of the NLP model at the time the expression was said. This might differ from the score that the current NLP model gives this expression

Add customer expressions to your model

To add a user expression to your NLP model:

1. Find the line of the expression.

2. There are 2 possibilities:

   • If you're satisfied with the expression and intent suggested, click on +.

   • If you're not satisfied with the expression and intent, you can edit them by clicking on the Edit button before adding that expression to your model.

Good practices before adding expressions

The image below summarizes the good practice to have when using the Train page and adding user expressions to your NLP expressions.

Check that the labelled intent is correct

Before you add an expression, you need to make sure that it's a relevant one. Not all things said by a user are qualitative enough for the bot to train on.

For example, consider the following expression, said by a user to your bot:

Even though our NLP is really smart, it doesn't always suggest the correct intent. In this case, the bot did not contain an intent referring to the date or time, which is why the NLP classified the expression under a wrong intent. This is why you should always check an expression before adding it to the NLP.

Scope the expressions

We also recommend scoping expressions in case they contain unnecessary information. Let's look at the following example:

In this case, the NLP did find the correct intent, but the confidence score is rather low because the expression contains a lot of unnecessary information. It's best practice to delete this information from the expression before adding it to the intent, so that the bot trains on relevant info only.