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
- 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/ NLU? Why are these areas (doesn't have to be ones I've listed) so tough to figure out?
Edit 12/9/2019: For anyone who wants to put an answer / opinion, please do. I feel this question benefits from a lot of different perspectives and as new technologies arise answers will be different.