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Design a Multi-Lingual Chatbot on Microsoft Azure

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Let us take an example of Chatbot which is support to handle 15 languages. Following are the challenges —

  • A Language expert — To properly train a Chatbot for perfect response, one has to engage a Language expert and train the LUIS and QnA maker to understand and provide best fit response. Further, there is a need to create a resource file to provide user with a response in the language of their preference. For 15 languages, you would need 15 experts, unless someone knows multiple of those in the list 😋

Your approach to solve the requirements depends on what you have in terms of language expertise, no. of languages to serve, cost implications and performance. Following are two approaches possible —

1. How Businesses are Winning with Chatbots & Ai

2. 21 Best Telegram Bots That Everyone Should Know

3. 12 Amazing AI Chatbot Trends for Business in 2019

4. The road to a conversational banking future

Given you have following challenges and requirements –

  • Less Budget for Azure Services and maintenance

For above scenario, one can use Microsoft’s Text Translator Service which support text translation for more than 30 languages (Refer official docs for more details). This approach follows simple steps –

if language = English:
then{
a. GET intent from LUIS OR GET response from QnA
b. Display response to user
}
if language != English:
then{
a. Convert the text to English using Microsoft Translator
b. GET intent from LUIS OR GET response from QnA
c. Convert the response back to preferred language using Microsoft Translator
d. Display response to user on chat
}
Design Workflow for Multi-Lingual bot with Text Translator Service (Not architecture diagram)

Advantages of using Microsoft Translator Service

  • None or less dependency on Language expert

Disadvantages of using Microsoft Translator Service

  • Lack of Perfection — Text translator does not have complete human parity on language translation and hence can jumble words and can kill the intent or meaning out of the sentence which can embarrass the bot 🤕 and impact user experience

Given you have following requirements –

  • Lack of confidence on Text Translator service to translate text with perfection and without loosing or jumbling intent and entities

For above scenario, one case use the approach of training LUIS and QnA service for each language of preference. The approach follows the sample steps –

if language = English:
then{
a. GET intent from English LUIS or English QnA Service
b. Display response to user
}
if language = Spanish:
then{
a. GET intent from Spanish LUIS or Spanish QnA Service
b. Display response to user
}
.
.
.
And so on......
Design Workflow for multiple LUIS and QnA Case (Not an architecture diagram)

Advantages of using Language specific LUIS and QnA Approach

  • Better performance and reduced latency by removing extra processing time required in language translation

Disadvantages of using Microsoft Translator Service

  • Cost — Having multiple services for LUIS and QnA would cost extra



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