@chowdhuryshakur Have you tried to create intents in other languages along w/ the intents in English for the same bot? With multilingual dense featurizers like rasa/LaBSE, it should work.
Another way is to detect the language of incoming texts, save it as metadata, translate the texts to English, generate the corresponding response and translate it back to the original language. But the previous option looks safer for low-resource languages even if it spawns many identical intents in other languages.
To make your Rasa bot multilingual, there are several approaches you can take. Here are a few options:
Use machine translation: One approach is to use machine translation to translate the user’s input from their language into English, and then process it with your existing English-language Rasa bot. You can then translate the bot’s response back into the user’s language. There are several machine translation services available, such as Google Cloud Translation, Microsoft Azure Translator, and Amazon Translate.
Train multiple models: Another approach is to train multiple Rasa models, each for a different language. You can use the same training data but provide translations for each language. You will need to create language-specific NLU and response templates for each language.
Use pre-trained models: You can use pre-trained language models, such as spaCy or Hugging Face’s transformers, which support multiple languages. These models can be used to process the user’s input in their language, and then the output can be passed to your Rasa bot.
Here are some steps to follow for each approach:
Use machine translation:
Integrate a machine translation service into your bot’s pipeline. You can use the rasa-nlu HTTP API to connect with external services like Google Translate.
Define a language detection component to detect the user’s language, which will be used to translate their input into English.
Train your Rasa bot on English data and use the translated text as input.
Once your bot provides a response, translate it back into the user’s language.
Train multiple models:
Create a language-specific NLU and response templates for each language you want to support.
Train a separate Rasa model for each language using the translated data.
Use language detection to route the user’s input to the appropriate model.
Use pre-trained models:
Use a pre-trained language model to detect the user’s language.
Pass the detected language to the corresponding language-specific NLU pipeline, which can be defined in your Rasa bot configuration file.
Once your bot provides a response, pass the output to the pre-trained language model to generate the response in the user’s language.
Note that these are just general guidelines, and the specific implementation will depend on your use case and the resources available to you.