What are the current big challenges in natural language processing and understanding? I'm doing a paper for a class on the topic of big problems that are still prevalent in AI, specifically in the area of natural language processing and understanding. From what I understand, the areas:


Text classification
Entity recognition 
Translation
POS tagging


are for the most part solved or perform at a high level currently, but areas such as:


Text summarization
Conversational systems
Contextual systems (relying on the previous context that will impact current prediction)


are still relatively unsolved or are a big area of research (although this could very well change soon with the releases of big transformer models from what I've read).

For people who have experience in the field, what are areas that are still big challenges in NLP and NLU? Why are these areas (doesn't have to be ones I've listed) so tough to figure out?
According to a nice article by Sebastian Ruder https://ruder.io/4-biggest-open-problems-in-nlp/ based on answers from top NLP researchers https://docs.google.com/document/d/18NoNdArdzDLJFQGBMVMsQ-iLOowP1XXDaSVRmYN0IyM/edit


Natural language understanding
NLP for low-resource scenarios
Reasoning about large or multiple documents
Datasets, problems, and evaluation


I recommend having a look at the article. More details in the slides https://drive.google.com/file/d/15ehMIJ7wY9A7RSmyJPNmrBMuC7se0PMP/view