All machine learning engines (including the ones that make chatbots work) need training data to be useful. The better the training data is, the better results you will get. What’s a data scientist to do if they lack sufficient data to train a machine learning model?
So what’s the problem? Getting appropriate training data is not an easy task. The data has to fit exactly with your needs; if you have an online store, for example, you will need data from users of other online stores, and your competitors are not going to give you theirs. Additionally, if you are not a large company with thousands of users, the data you will get from your own users will be scarce; and your ML engine is going to need LOTS of data to work well.
The solution most people use for this is to get brand new training data by making other people create it from scratch. There are sites like the Amazon Mechanical Turk that allow you to put hundreds of people to work, creating the data you need: “Hi, please write several thousand sentences as if you were buying in my online store, thank you”.
This seems to be a feasible solutions, but it has many drawbacks: this manual data is expensive; it takes a lot of time to collect it; you have to constantly ensure its quality is good enough… And apart from this, it’s not reusable: if your online store sells shoes, and one day you want to start selling suits, you will have to redo most of the training data, because it won’t fit to your new needs.
The solution? Let the machines write your training data. Bitext’s Synthetic Data Service will create all the training data you need, tailored to your needs, in a much shorter time than you could possibly get via manual work, and in a way that is easily reusable for you whenever you need it. Visit us and download a test dataset to see how synthetic/artificial data works for your case.