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I'm still getting up to speed with machine learning, but I'm aware of the papers on joint intent detection and slot filling by Bing Liu & Ian Lane, and another by Xiaodong Zhang and Houfeng Wang - and I'm sure there would be others.

There are several implementations available on GitHub:

I'm hoping to be able to train a model using python and TensorFlow on the back-end, and then use the model in Node and browsers using TensforFlow.js

If I feed the training process with a limited set of patterns of the form:

flights from @City:origin to @City:destination @Date:date
flights from @City:origin to @City:destination @DatePeriod:datePeriod
airfares from @City:origin to @City:destination
...

...then the vocabulary would be fairly limited:

  • flights
  • from
  • to
  • airfares

Plus known entity values:

  • Boston, New York...
  • tomorrow
  • next week
  • saturday...
  • first...
  • january...

I have a few noob questions:

  • Is this strategy valid?

  • If my vocabulary is, say, 100 words then how big is the model that would have to be downloaded by the web app?

  • How difficult would it be to implement the .predict() side in TensorFlow.js?

  • Would there be any noticeable performance difference between the TensorFlow.js .predict() implementation and iterating through a list of regular expressions?

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