The Power of Owning your Training Data

Several months is what it takes before you have a well-trained bot ready to go. First, you must generate a dataset in a bot building platform with a specific format. Then, you should test it to see if it works. Not working properly? Go back to the beginning and start over. Quite hard, right? Ok, now try with another platform and do it from scratch once again. Results? Cranky developers and a bad bot. Don’t you think is about time to say hello to your own platform-independent artificial training data?

Business from the hospitality, health or banking sectors, have huge volumes of data at their disposal, but they mainly present an unstructured form. All that data coming from e-mails, chat scripts or public sources is there for free, however, its quality leaves a lot to be desired... People used to think that getting good training data means a lot of time and money and these were just available for major languages like English. That was, nevertheless, an issue from the past; now, things have changed. Thanks to brand-new natural language generation tools, owning your training data is easier than ever before and it will help you train any AI-powered virtual agent in a matter of seconds with no effort at all.


When talking about AI, a shortage of training data is a big issue and bots need to be fed with great amounts of high-quality data. This, combined with the fact that most of that data must be anonymized, decreases, undoubtedly, the volume of data available at the end. What’s more, if we add that the chunks of text are not in the format required by the platform you use, you will get caught in a never-ending work loop. In such cases, the right thing to do is making up your mind and choosing between having a bot with poor understanding skills or creating your own data to train a personalized model for your specific purpose.  Which alternatives do you have if choosing the last option?

Generate Your Own Training Data

If your business needs a great deal of customized data, the only option for you would be to create your own training datasets. Let’s think of a company willing to build an e-commerce bot for women fashion. If its model is intended to perform something more specific (e.g., special offers or extra sizes), they will surely need their own training data tailor-made to meet their needs. Here you have three different ways to get those data:

  • In-house manual creation of data.
  • Manually-generated data from third-parties.
  • Automatically-generated artificial training data.

Needless to say, the best option would be the last one. In the first case, all possible variations of a sentence needed for the bot to properly understand the user are not the only problem, but also negative phrases, polite set expressions… Doing all that manually is not just quite hard, we’d rather say it’s impossible. Even if a company had hundreds of interns, as it may be the case in the second option, it wouldn’t be possible anyway. There will be something missing, even though. Why doing something manually when you can automate the process? Through Bitext tools to generate artificial training data, you will have full control over your data that will be always at your disposal to be modified. Any change made will automatically be adapted to any platform format since your data will present a neutral format and, with a click of a button, they can be turned into the format supported by any other platform.


Maybe you are right when thinking that bot building platforms as DialogFlow or Rasa already have some datasets at one’s disposal. Nevertheless, these are usually very simple structures which have been created manually, so, after all, the amount of data won’t be enough.


Therefore, the fact of owning those great volumes of data without the need to generate it by hand is just priceless. So, please stop complaining about how difficult and time-consuming getting enough training data for your bot is, and start doing something about it. Contact us here, we will make it easier for you.


NLG, NLP for Core

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