Happy to talk more offline, my email is my profile. The short answer is no because there are more complexities involved - both related to our specific use cases but really natural language in general. If that were the case NLP would be solved and any company that could exist would already. From my experience, I'm not sure where the line is between choosing the right model vs having the right data solves most problems. There have been novel architecture developments like rnns and lstms that have shown well to support certain domains. New architecture are developing each year and the space moves very quickly. On the flip side, having pedabtyes of data (like BERT or OpenGPT) and simpler architectures is also powerful but prohibitive to everyone that is not Google or state government. The real answer is probably somewhere in between and whiles it's unsolved, there is work for me to do. That being said, our strategic philosophy is to make our AI a commodity so that we can differentiate ourselves on other features.

Most of our problem don't cleanly map to existing NLP tasks. State of the art often isn't as high as you think in many tasks. For example, the machine translation in relation to beta feature we're building that lets you ask the question of arbitrary single tables (kind of like wiki-tables) but we don't the know the schemas in advance or the questions the user may ask about. Outside of having the issue of having quality annotated data (which we often don't - cold start problem), we need to do more than simple model tuning. It requires building custom architectures.

But even when you consider known tasks, state of the art models do not often produce those same results on real-world data. If you put aside data quality issues (which is another huge challenge for us), in the context of question answering, the training data rarely captures the distribution of the natural language in the wild. People ask questions differently and use language that doesn't match the content in our knowledge base.

I could go on. But short answer, it's not as straightforward as you think. Even at google scale, machine learning is not solved. For everyone else with fewer data and domain-specific use cases, it's even harder.