From a practical perspective, there are many benefits here: developers don’t have to be concerned with selecting the best algorithms for their classification problem, there is no need to scale the implementation, there are existing efficient user interfaces and upgrades, and optimizations are seamless. If you are creating a AI system and need the basic classification and entity extractions features, using a cloud-based service is the best option.
-
Microsoft’s Language Understanding Intelligence Service (LUIS): This is the purest example of an LU system because it is completely independent from a conversation engine. LUIS allows the developer to add intents and entities, version the LUIS application, test the application before publishing, and finally publish to a test or production endpoint. In addition, it includes some very interesting active learning features.
- Google’s Dialogflow (Api.ai): Dialogflow, previously known as Api.ai, has been around for a while. It
allows the developer to create NLU models and
define conversions flows and calls to webhooks or cloud functions when certain conditions are met. The conversation is accessible via an API or via integrations to many messaging channels. -
Amazon’s Lex: Amazon’s Alexa has long allowed users to create intent classification and entity extraction models. With the introduction of Lex, Amazon brings a better user interface to NLU with bot development. Lex has a few channel integrations at the time of this writing and can be accessed via an API. Like Dialogflow, Lex allows developers to use an API to talk to the bot.
• IBM Watson Conversation: Yet another similar system, Watson Conversation allows the user to define intents, entities, and a cloud-based dialog. The conversation is accessible via an API. At the time of this writing, there are no prebuilt channel connectors; a broker must be written by the bot developer though samples exist.
• Facebook’s Wit.ai: Wit.ai has been around for a while and includes an interface to define intents and entities. As of July 2017, it is refocusing on NLU and removing the bot engine pieces. Wit.ai is also being more closely integrated with the Facebook Messenger ecosystem.
Source:
Rozga, Szymon. (2018). Practical Bot Development: Designing and Building Bots with Node.js and Microsoft Bot Framework. 10.1007/978-1-4842-3540-9.
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