Bots built upon machine learning need long training processes to have the ability to hold a meaningful conversations with real people. Training data becomes, therefore, a diamond in the rough; all companies need such input for their bots.
Until now, this data was generated in a slow manual way. However, speeding up your bot training can now come true with artificially generated data.
Everybody knows that a manual generation of training data turns out to be a never-ending task that leads to error-prone results. Perhaps not the right one for the main purpose of any successful enterprise: owning a bot able to understand every single query made by a user.
That’s to say, if you want your bot to recognize a specific intent, you must feed it with a great number of sentences alluding to it, which can end up being prohibitively expensive.
Following the above mentioned manual procedure, it takes plenty of time and money to have enough content available for a successful human-robot interaction.
Nevertheless, teaching bots how to talk properly is easier than ever before and some companies are already getting on board aiming to automate this time-consuming process with artificial training data.
This process makes it possible for them to reduce the cost and time wasted in generating data for Machine Learning training.
Artificial training data: companies working on it
- TwentyBN. This image recognition system enriches its training potential by generating videos in a data factory so that neural networks learn about the real world. Its technology allows to analyze human movements and extract real-time information building, at the same time, deep learning systems using datasets about common world situations.
- Spil.ly. This startup makes use of artificial training data extracted from graphic contents. At first, lots of machine-learning algorithms were needed to track human bodies in videos and they couldn’t afford to pay for it. That’s when its engineering team originated their own labeled images to train the algorithms by applying methods used in filmmaking and videogame graphics.
- Bitext. In like manner, but textually speaking, we can facilitate those companies in need of big amounts of data to automatically generate many different variations from one sentence in order to automate the most laborious part of a bot enhancement process. This variant generation tool creates multiple alternatives for each query of a training dataset by including, for instance, polite set expressions or morphological and syntactical switches. This approach is bringing such good results as improving the understanding accuracy of a Rasa built bot by 30%.
This auto-generated artificial training data serves as ‘food for thought’ for bots enabling them to recognize and categorize every intent of a sentence successfully. Bitext system will also noticeably increase the accuracy of your bot reaching really good results.
Such artificial training data can incredibly improve the results of ML-based bot platforms when comparing a bot trained with manually-processed sentences with another one trained with thousands of sentences generated via Bitext technology.
We are currently developing a brand-new, ground-breaking computer science that allows machines to see the world as humans do. Just think of it as if the artificial is embedded in artificial intelligence.
Why not? Enhance your bot performance on the spot by automating its data training.