Increasing bot accuracy has never been so easy. How? Generating artificial training data, not manually, but using auto-generated query variations. We have benchmarked Rasa and other platforms, and their accuracy comes up to a 93% thanks to Bitext artificial training data tech.
In the past weeks a lot has been said about last Google I/O's presentation of Duplex, an assistant powered by AI that can make phone calls and talk to humans to make arrangements for you. Some people are so impressed by the achievement that they are already pointing out the ethical consequences of not being able to tell apart a human from a machine, and some are playing it down, highlighting that we have only seen a demo.
What happens after you build a chatbot for a client? I mean, when the architecture is set up, the NLU module has been built, the conversation flows are designed, and everything is ready. All is left is going into production, where you will have thousands of users' interactions that will train your bot and make it super smart, right?
We have already talked a lot in this blog about training chatbots, the issues bot builders encounter in this task and our tips to enhance its performance, no matter the NLU platform they are built on. Dialogflow, Lex, LUIS, we have studied them all.
Some of you have asked for more details about the TechCrunch's Messenger bot upgrade, so we decided to share them here. Thus, in this article we will disclosure what exactly did the improvement consist on, along with some query examples and how this evolution to a more conversational bot was possible.