Data scientists from companies all over the world are racking their brains to collect data and turn it into cash flow. One good example is the great volume of social media data used to train AI assistants. That’s not a problem anymore, however, thanks to an artificial training data approach. Surprisingly, several industries like finance, retail or healthcare are already getting on board by using synthetic data to create innovative products and services in their fields. Why do they do that? These companies aim to automate this time-consuming process to reduce the cost and time wasted in generating data for Machine Learning training. Nvidia also supports this approach in its e-book called Synthetic Data Will Drive the Next Wave of Deep Learning Applications.
At Bitext, we can facilitate those companies in need of huge amounts of data by applying our Natural Language Generation technology. Generating many different variations from one sentence, the most arduous part of building a chatbot becomes automated. Our variant generation tool creates multiple alternatives for each query of a training dataset by including morphological and syntactical switches or polite set expressions for instance. Thus, we take a seed sentence and automatically generate many different variants with the same meaning, like the example seen in the chart below.
This auto-generated artificial training data, as seen in our previous post, serves as ‘food for thought’ for bots. It helps them recognize every intent of a sentence and thus, reach really high accuracy. Therefore, artificial training data can incredibly improve the understanding skills of any ML-based bot, especially if we compare it with a manually-trained bot. Here is where Bitext comes to the rescue with its NLG technology, which can be implemented not just for English, but for any language. Isn’t a groundbreaking invention?
Take a look at our chatbot automation solutions and see for yourself how the understanding skills of any bot could be enhanced up to 90% accuracy. Don’t you think is time to stop being simple data harvesters and evolve into data farmers?