The question is, can you build useful NLP systems with machine learning methods? And the answer appears to be a pretty clear 'yes'. I assume the set of problems

The question is, can you build useful NLP systems with machine learning methods? And the answer appears to be a pretty clear 'yes'. I assume the set of problems such methods can solve, and the quality of those solutions, is only going to go up. Let them have their fun, I say. That said, I've yet to see a good question answering system even in 2021, and I have somewhat traumatic memories of my own attempts: https://www.youtube.com/watch?v=a2ufq6CdCYw https://www.youtube.com/watch?v=a2ufq6CdCYw The approach we settled on was quite old fashioned tree-based parsing of questions, with quite a lot of help to guide users to the subset of English we actually understood (disambiguation of both sentence structure and arbitrary named entities in a customer database, and upgrading partial/keyword queries to full sentences to be clear what we thought we were answering etc). All this was built on top of quite carefully curated customer ontologies and mappings to verb frames. We always intended to one day infer most/all that using machine learning when we had enough curated data to train and get a feel for how well it worked but we quickly ran out of oomph. I have some sympathy with the author's basic point. I know there are pretty good demos of NLP over tabular data these days but I still think they'd struggle with any interesting real world usage (i.e. replacing SQL), especially when you have to model relationships between data in an arbitrary database. If you're anything less than 100% accurate in translating user queries, you have to provide ways to let them nudge your system in the right direction. It's true that human communication isn't necessarily entirely clear to even a human listener, but people know what they _mean_ to say, and you do have to offer some way to let them say it eventually. I find more mechanistic parsers to be more amenable to that, in general (but I'm not really up to date on the literature these days). I'd totally be in the market for such a system today if anyone can point at something robust though.