True, we have been building conversational interfaces with traditional NLP. In my experience, they’ve been fairly fragile.
Extending the example you gave, nicely packaged, fully deterministic workflows work great in demos. Then customers start going off the paved path. They ask about returning 3 items all at once, or a whole order. They get confused and provide a shipping number instead of an order number. They switch language part of the way through the conversation because they get frustrated by all these follow-up questions.
All of these absolutely can be handled through traditional NLP, but require system designers to account for them, model the conversation, and design their system accordingly to react accordingly. And suddenly the 5-6 step deterministic workflows with a couple of if-branches… isn’t.
Extending the example you gave, nicely packaged, fully deterministic workflows work great in demos. Then customers start going off the paved path. They ask about returning 3 items all at once, or a whole order. They get confused and provide a shipping number instead of an order number. They switch language part of the way through the conversation because they get frustrated by all these follow-up questions.
All of these absolutely can be handled through traditional NLP, but require system designers to account for them, model the conversation, and design their system accordingly to react accordingly. And suddenly the 5-6 step deterministic workflows with a couple of if-branches… isn’t.