Oracle Digital Assistant – Multilingual the easy way

The Oracle Digital Assistant (ODA) provides out of the box Native support for several Languages. In this post I will cover the necessary steps to implement a pure Multilingual Chatbot.

“You can design both single-language and multi-language skills. For understanding user input, you can either use Oracle Digital Assistant’s native support for various languages or a translation service. For output, you typically define the strings for each target language in resource bundles.“

The documentation is a good place to start and understand all the details around the Multilingual support. There are typically two approaches, (1) use translation services or the (2) native language support.

Here I will focus on the native language support and cover all the necessary steps to have a fully working multilingual chatbot.

Create Skill

As always the first step is to create a Skill 🙂

We always need to have a default primary language – in this case English

From the Drop Down we can see the available supported native languages.

Create Intents

For this demo I will create a Greeting Intent. Notice that we can only enter Utterances for the chosen English Language.

Add Additional Languages

Under Configuration/General Add more languages

After the above step, we can go back to the Intents page and we see the option to enter Utterances in the additional language.

Resource Bundles

The support for native languages only works for the ODA to understand user input. The approach for bot output is via resource bundles. Think of this as lookup tables. Here we can provide the output in available languages.

I create a resource bundle with a Greeting01 key, for the default (English) language.

Then for the same key, I add a new entry for Portuguese.

At the end it should look like the picture below. The principle is the same, whether we support 2 or all the native languages.

If you try to train the model at this time, you will see an error stating that we need at least 2 Intents in order to train the model. Hence, I add another Intent (Exit)

Dialog Flow

The dialog flow is where we glue all this pieces together. The most interesting part is that the ODA automatically identifies the language, and the only thing we need to do, is to pass the reference to the resource bundle.

In the below code, we Identify the Intent (Greeting) and transition to the state PrintGreeting, which is an output component*. The text has the reference to the resource bundle: text: “${rb.Greeting01}”

We don’t need to worry about language detection, the ODA will take care of that and pick up the appropriate language .

*I just had my ODA updated to 21.06 and it seems the default output component is now System.CommonResponse, but one could also use the System.Output.

main: true
name: ML_Demo
context: 
  variables:     
    iResult: "nlpresult"

states:
  intent:
    component: "System.Intent"
    properties:
      variable: "iResult"
    transitions:
      actions:
        greeting: "printGreeting"
        exit: "exit"        
     
  printGreeting:
    component: "System.CommonResponse"
    properties:      
      keepTurn: true      
      metadata:
        responseItems:        
        - type: "text" 
          text: "${rb.Greeting01}"   
    transitions:
      return: "done"  

  exit:
    component: "System.CommonResponse"
    properties:
      metadata:
        responseItems:
          - type: "text"
            text: "Sorry, I am giving up!"
    transitions:
      return: "done"

Testing the Conversation

This is the principle behind a Multilingual implementation – ODA gives us the tools to simply implement a true Multilingual chatbot with minimum effort.

For more information please ask Artie -> the ODA Documentation Chatbot.