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Back-off to general models when using grammars

In our use of STT for Conversation Watson Assistant, we had many cases (responses expecting codes etc) where the grammar functionality would be extremely useful useful but for natural deployment a "fall back" to general  recognition models would be needed. The particular form of the codes we were looking at for this customer was things like "R01" or "R23", which are problematic for reasons that should be obvious.  This would allow us to support both interactions of the form:


Watson: "Okay, what is your error code?"
User: "R23"
....

As well as:

Watson: "Okay, what is your error code?"
User: "I'm sorry, can you tell me where to find that?"
.....

  • Guest
  • Mar 22 2018
Why is it useful?
Who would benefit from this IDEA? As a STT customizer, I could provide my Watson Assistant colleague with a customization of the STT service that is effective recognizing short structured inputs as well as general inputs
How should it work?
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