Cross-lingual classification

So far, you have learned that cross-lingual models are capable of understanding different languages in semantic vector space where similar sentences, regardless of their language, are close in terms of vector distance. But how it is possible to use this capability in use cases where we have few samples available?

For example, you are trying to develop an intent classification for a chatbot in which there are few samples or no samples available for the second language; but for the first language—let's say English—you do have enough samples. In such cases, it is possible to freeze the cross-lingual model itself and just train a classifier for the task. A trained classifier can be tested on a second language instead of the language it is trained on.

In this section, you will learn how to train a cross-lingual model in English for text classification and test it in other languages. We have selected a very low-resource language known...