One of the essential tools of Conversational AI is Natural Language Understanding (NLU). This is what allows Intent Builder to analyze consumer input and assign accurate intents.

While LivePerson provides its own proprietary NLU out of the box, Intent Builder also allows you to choose your preferred NLU Engine for analyzing text by routing all NLU analysis and training through an API. This API layer of abstraction means you can choose from the following NLU engines:

  • LivePerson's native NLU
  • Google Dialogflow
  • IBM Watson

If you choose LivePerson's native NLU, no setup work needs to be done to connect the NLU engine to your domain in Intent Builder. Third-party providers require an additional setup process that's outlined farther below on this page.

Language support

LivePerson NLU supports intent detection for English and Spanish.

Available with IBM Watson:

  • Arabic
  • Chinese, Simplified (China)
  • Chinese, Traditional (Taiwan)
  • Dutch (Netherlands)
  • English (non-region-specific, Australia, Canada, Great Britain, India, United States)
  • French (Canada, France)
  • German (Germany)
  • Italian (Italy)
  • Japanese (Japan)
  • Korean (Korea)
  • Portuguese (Brazil)
  • Spanish (Mexico, Spain)

Available with Google Dialogflow:

  • Chinese, Cantonese (Hong Kong)
  • Chinese, Simplified (China)
  • Chinese, Traditional (Taiwan)
  • Danish
  • Dutch (Netherlands)
  • English (non-region-specific, Australia, Canada, Great Britain, India, United States)
  • French (non-region-specific, Canada, France)
  • German (Germany)
  • Hindi
  • Indonesian
  • Italian (Italy)
  • Japanese (Japan)
  • Korean (Korea)
  • Norwegian
  • Polish
  • Portuguese (Brazil, Portugal)
  • Russian
  • Spanish (non-region-specific, Latin America, Mexico, Spain)
  • Swedish
  • Thai
  • Turkish (Turkey)
  • Ukranian

LivePerson's NLU engine

There are two versions of LivePerson's NLU engine: version 1 (v1) and version 2 (v2).

LivePerson NLU v1

Key characteristics include:

  • A recommender model based on Word Mover's Distance (WMD) algorithms.
  • Considered an "entry level" NLU engine because it's more specific. In other words, for the v1 algorithm to work well, the sample sentences should be close to the expected user input and have only small differences in wording, for example:

    Expected user input: I want to buy a brown Michael Kors bag
    Tailored sample sentence (with entities): I want to buy COLOR PRODUCT_BRAND bag

    In contrast, NLU v2 is more generalized; it can handle a general set of user questions and still perform well.

  • From an NLU processing perspective, performs well regardless of the number of intents and training phrases involved. However, if you have more than 5 intents and more than 20 training phrases per intent, there is a degradation of speed at runtime when processing the user inputs.
  • For performance reasons:
    • Supports a maximum of 40 training phrases per intent. If you add more than 40, only the first 40 are used.
    • Supports a maximum of 20 positive learnings per Knowledge Base article. If you add more than 20, only the first 20 are used. There is no limit on the number of negative learnings; however, see the best practices discussed here.
  • Doesn't require the model to be trained, which can save time.
  • Doesn't support prebuilt domains or Regular Expression entities.
  • Can't be used with Intent Analyzer.
  • Supports English or Spanish.

LivePerson NLU v2

Key characteristics include:

  • A classifier model based on Convolutional Neural Network (CNN) using Fasttext embeddings.
  • Its primary feature is the enabling of a separate brand-specific model, built and trained for each domain.
  • A scalable solution that can handle a greater volume of requests, providing faster response times and accuracy.
  • To perform effectively, expects large sets of data (both intents and training phrases).
  • When you create a domain with NLU v2 and use it in Intent Analyzer or in Conversation Builder, the following is required:
    • At least 20 training phrases per intent
    • At least 5 intents in order to train

    If your domain complies with these requirements, LivePerson recommends that you use LivePerson NLU v2 (not v1) if possible.

  • Requires the model to be trained.
  • Supports prebuilt domains and Regular Expression entities.
  • Can be used with Intent Analyzer.
  • Supports English.

Connect a 3rd-party NLU engine

3rd-party NLU limitations

  • Doesn't support prebuilt domains or Regular Expression entities.
  • The length of the domain name should not exceed 64 characters. (Watson limitation)
  • A domain can only support one language, which is specified on the Domain Settings page.
  • LivePerson does not support "pulling" into Intent Builder existing models that have been trained in IBM Watson or Google Dialogflow. Only model "push" is supported; this is accomplished by training the model in Intent Builder.

Step 1: Enable 3rd-party NLU support

Contact your LivePerson account administrator to enable your account for 3rd-party NLU support.

Step 2: Sign up and get the API keys

Repeat this step twice to create two sets of IBM Watson or Google DialogFlow service credentials. When you train the intents in a domain for the first time in Intent Builder, you'll use the first set of credentials. Those credentials will then be active for the first model version that gets created. Since only one set of credentials can be active at a time, you'll need to use the second set of credentials the second time you train. And with each subsequent training, you'll need to alternate back and forth between the credentials.

IBM Watson
  1. Register for or log in to an IBM Cloud account.

  1. Create or access a Watson Assistant resource.

  2. Generate Service Credentials with the role of Manager and an Auto Generated Service ID.

  3. View and copy the newly created credentials.

Google Dialogflow
  1. Log in to the Dialogflow console.

  2. Create a new Dialogflow agent (which will create a new Google project).

  3. Create a new Service Account for the newly created Google project with the role of Dialogflow API Admin.

  4. Create a JSON-formatted private key for the service account by clicking the Create key.

  5. View and copy the created key. This will be used in your Dialogflow config data.

Step 3: Add a domain for the 3rd-party NLU provider

In Intent Builder, add a domain that uses the 3rd-party NLU engine as its NLU provider. You can import the intents and entities at that time or add them later but before proceeding to step 5.

Step 4: Create the NLU provider credentials

In Intent Builder, in the domain that you created in the previous step, create the NLU provider credentials for the 3rd-party NLU engine. This is when you'll copy and paste in the credentials that you downloaded from IBM Watson or Google Dialog flow (step 2 above).

Step 5: Train the domain

In Intent Builder, train the domain. Once training is completed (which creates the model version), you can start to test.