One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.
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While AI is one of the most important trends nowadays, there are still challenges to overcome. Apart from common technical issues such as a lack of quality data, there is much beyond its abilities for an AI to effectively understand and react when it comes to human-machine interaction.
The upgrade we performed to TechCrunch’s Messenger bot turns 1 month today. It’s been 4 weeks since we integrated our NLP middleware into the existing bot architecture to make it benefit from our query rewriting technology, so it’s time to look at the effects it is taking.
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.