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Microsoft is making big bets on chatbots, and so are companies like Facebook (M), Apple (Siri), Google, We Chat, and Slack.
Retrieval-based models (easier) use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context.Microsoft recently released their own bot developer framework.Many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible.Look up League of Legends ranks, the now playing song on Spotify/Last.fm, the weather, and a lot more using variables.Variables allow you to create dynamic responses to commands and timers by accepting user input and querying remote APIs.Generative models (harder) don’t rely on pre-defined responses. Generative models are typically based on Machine Translation techniques, but instead of translating from one language to another, we “translate” from an input to an output (response). Due to the repository of handcrafted responses, retrieval-based methods don’t make grammatical mistakes.
However, they may be unable to handle unseen cases for which no appropriate predefined response exists.
The heuristic could be as simple as a rule-based expression match, or as complex as an ensemble of Machine Learning classifiers.
These systems don’t generate any new text, they just pick a response from a fixed set.
For the same reasons, these models can’t refer back to contextual entity information like names mentioned earlier in the conversation. They can refer back to entities in the input and give the impression that you’re talking to a human.
However, these models are hard to train, are quite likely to make grammatical mistakes (especially on longer sentences), and typically require huge amounts of training data.
Our integrations help you use your data from existing website to feed your chatbot.