You have probably used Siri, Alexa, or Cortana to set an alarm, call a friend, or arrange a meeting. But despite their usefulness in common and routine tasks, it’s difficult to force conversational agents to talk about general, sometimes philosophical topics. The Statsbot team spoke with data scientist Dmitry Persiyanov to learn how to fix this issue with neural conversational models and how to build chatbots using machine learning.
Interacting with a machine via natural language is one of the requirements for general artificial intelligence. This field of AI refers to dialogue systems, spoken dialogue systems, or chatbots. The machine needs to provide you with an informative answer, maintain the context of the dialogue, and be indistinguishable from the human (ideally).
In practice, the last requirement is not yet reachable. But luckily, humans are ready to talk with robots if they are helpful — sometimes, they can even be funny and interesting interlocutors.
There are two major types of dialogue systems: goal-oriented (i.e. Siri, Alexa, Cortana, etc.) and general conversation (i.e. Microsoft Tay bot). The former help people to solve everyday problems using natural language, while the latter attempt to talk with people on a wide range of topics.
In this post, I will give you a comparative overview of general conversation dialogue systems based on deep neural networks. I will describe main architecture types and ways to advance them. Also, there will be a lot of links to papers, tutorials, and implementations.
I hope this post will eventually become the entry point for everyone who wants to create chatbots with machine learning. If you read this post till the end, you will be ready to train your own conversational model. Ready?
... read the full article for the rest ...
Conversational models may seem difficult to grasp at first (and not only at first). I advise you to read the resources I gave links to. Also, there is a pool that contains many essential papers on dialogue systems.
When you’re ready to practice, choose some simple architecture, take one of the popular datasets or mine your own (Twitter, Reddit, or whatever), and train a conversational model on it.
Read the full article at https://dzone.com/articles/chatbots-with-machine-learning-building-neural-con
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