If you have trained a chatbot yourself or you have read our previous posts about the topic for Dialogflow (former API.ai) and LUIS, you already know that creating a functioning chatbot is a long and tedious process. On top of the complexity, even if you fulfill an “appropriate” training, this does not ensure that you will obtain a chatbot able to understand various inputs from users.
As Amazon makes its first foray into the Asian market by releasing Amazon Echo in India and Japan, we can’t help but ask ourselves: Does Alexa work in India? What is the present and future of Chatbots and VAs?
Chatfuel is one of the major chatbot development platforms with thousands of bots published and working. Their success cannot be discussed, however for nonspecialized users, is hard to achieve the development of a fully conversational bot. Why? Because it takes too much time to come up and tag manually all the sentences the bot must understand.
A chatbot trained with 1 billion user requests sounds like science fiction, particularly in a field like AI, where scarcity of training data is a widespread problem. Bots need to be fed data that:
Lately, we have been talking a lot about Natural Language Generation and how revolutionary it can be for chatbot training. But, what exactly is it? In this article, I am going to explain to you the fundamentals of NLG and how quickly it can be applied to Artificial Intelligence. The power of NLG combined with the one of AI will improve the results achieved by the industry.