What does cross-lingual NLP involve?

Cross-lingual NLP focuses on understanding text data in multiple languages, enabling systems to analyze, interpret, and generate text across different linguistic contexts. This involves techniques that allow for the transfer of information and meaning from one language to another, enhancing the ability to work with diverse languages in tasks such as sentiment analysis, information extraction, and text summarization.

While other options mention various aspects of language processing, they are more specific and narrower in scope. Processing images in multiple languages pertains to computer vision, translating audio data is a limited application of translation, and creating subtitles for videos is a specialized task that does not encompass the broader objectives of cross-lingual NLP. Thus, understanding text data in multiple languages serves as the foundation for cross-lingual applications and opens up opportunities for multilingual communication and accessibility in technology.