You have an NLP system like a search engine, chat bot, text classifier or NER model working for one language, like English, and now you want to make it work for more languages.

We'll go through the 3 basic approaches:

• Machine translating at inference (or query) time
• Machine translating labelled training data (or search indices), and training a multilingual model
• Zero-shot approaches with a massively multilingual language model like BERT or LASER

• Know when to use which approach
• Hands-on experience with each approach

• A Google Colab account
• Coding skills in a language like Python
• A GitHub account, a ModelFront account and a Unix system are recommended for applying these approaches in production
• If you'd like to check out the materials, code or datasets, you can take a peek at the workshop repo
• You can read up on multilingual search at https://modelfront.com/search