For sure. Many NLP scientific benchmarks are multilingual today and you need to evaluate on X languages. You are of course not expected to be fluent in all of t

For sure. Many NLP scientific benchmarks are multilingual today and you need to evaluate on X languages. You are of course not expected to be fluent in all of them. Most of the deep learning based approaches are quite language-universal, i.e. you have a pre-trained language model or word embeddings and you just slap a sequence modeling model on top of that and you let the neural network do the magic. However, you can still incorporate language-specific knowledge if you want to get the best performance for that one particular language. This is mainly true for academic research. If you are developing a rule-based or keyword-based application (which is still often the case outside of academia), it can actually help to be fluent to get the rules right.