They might even get higher accuracies with a dedicated classification layer. By using the existing vocabulary they are spreading the probability mass across a m

They might even get higher accuracies with a dedicated classification layer. By using the existing vocabulary they are spreading the probability mass across a much larger space. If they stuck to N options where N is the total number of functions available to the model I suspect they could get to 100% accuracy. It's also not clear whether there is sufficient ambiguity in the test data for this to be a generalizable model. The difficulty with "intent recognition" (which they don't mention but is what this problem is called for agents like Siri) is that human generated inputs vary widely and are often badly formed. If they haven't done extensive evaluation with human users and/or they've constrained the functions to be quite distinct then they aren't yet tackling a hard problem, they've just got a complex setting.