Implementing a Natural Language

Classier in iOS with Keras + Core ML

An iOS Swift, fully oine Natural Language Classier

(NLC) for implementing local in-app Intent

understanding with training dataset imported from

IBM Watson, Google DialogFlow, AWS Alexa/Lex, and

other Natural Language Understanding (NLU) platforms

Jacopo Mangiavacchi Follow

Apr 3, 2018·6 min read

Introduction

IBM Watson NLC and Conversation services (as well as many other

NLU cloud platforms) provide a Swift SDK to use in custom apps to

implement intent understanding from natural language utterances.

These SDKs and the corresponding NLU platforms are super powerful.

They provide much more than simply intent understanding capability

— they also detect entities/slots and provide tools to manage complex,

long running conversation dialogs.

However, even for the most basic NLC inference, these SDKs depend on

network connectivity, as the NLC model is run in the Cloud.

iOS already provides very ecient Text-To-Speech and Speech-To-Text

APIs (SFSpeechRecognizer and AVSpeechSynthesizer) fully capable of

working oine on iPhone/iPad devices. By using Core ML models to