Out of curiosity, why are you deploying to all your web servers simultaneously? Could you not do a partial roll-out to make sure something like this doesnt happen?
We have talked about it. It is unlikely to helped with an event like this, and I don't recall an event where it would have. It also has the downside of extending our deployment cycle by a lot. Notably, we do run a canary internally, and that had no issues, which actually through us off for a while because while the app was partially down for users it was working for us and that hasn't happened to us in a while.
What's the opposite of 'drawing from incomplete evidence'?
I hate the power Facebook as a behemoth and a monolith holds and abuses. But when accusing, we should be fair. The statements cited as evidence are from a publication, not an offhand comment by a random person on Facebook with no regard to correctness or clarity. And even in that, there isn't clarity for what was being accused.
So, if pointing out a lack of clarity in an accusation is called nitpicking, so be it.
I think your use of the term "understanding" is very unhelpful here. It's better to think about what you need to condition on to predict correctly.
In fact most intonation decisions are pretty local, within a sentence or two. The most important thing are given/new contrasts, i.e. the information structure. This is largely determined by the syntax, which we're doing pretty well at predicting, and which latent representations in a neural network can be expected to capture adequately.
The same sentence can have a very nonlocal difference in intonation.
Say, “They went in the shed”. You won't pronounce it in a neutral voice if it was explained in the previous chapter that a serial killer is in it.
On the other hand, if the shed contains a shovel that is quickly needed to dig out a treasure, which is the subject of the novel since page 1, you will imply urgency.
With enough labor, you could annotate enough sentences to cover a lot of dialogue cases. Sections like "'stop!', he said angrily/dryly/mockingly are probably fairly common. You'd be modeling the next most probable inflection given previous words and selected tones.
What would require understanding would be novel arrangements and metaphor to indicate emotional state. On the fly variations to avoid mononticity might also be difficult, as well as sarcasm or combinations/levels (e.g. she spoke matter of factly but with mirth lightly woven through).
And who says it can't understand the material? There have been recurrent networks trained that can translate between languages, or predict the next word in a sentence, at remarkable accuracy. Combined with wavenet this could be quite effective.
There could be cases where the intonation is dependent on things entirely outside of the book. If say a politician does something in the writing that is far from what we would expect them to do in today's world.
