Synthetic image and video have proven to be a big success for cost-cutting. Synthetic text is following suit: tabular data (that is the data organized in a table with rows and columns) is becoming mainstream already, and the next step is synthetic unstructured text, which is the data that doesn`t have a predefined format.
Synthetic unstructured text supports more complex cases, where actual text in the form of full sentences or documents is required.
Synthetic Training data is the data that is used to train an NLU engine. An NLU engine allows chatbots to understand the intent of user queries. The training data is enriched by data labeling or data annotation, with information about entities, slots…
In this blog we will discuss three ways of doing your chatbot evaluation by using:
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.
A chatbot offers significant advantages: it allows your customer support to be omnichannel —i.e. customers will be able to approach you via written chat, voice chat, email, phone, etc.—; it will save you the costs of hiring many additional employees, while sparing you the need of training agents every time your product changes; your customers’ satisfaction will increase; finally, you will get more and more sales (because well-taken-care-of visitors tend to become loyal and regular customers).
Let’s repeat: more and more people (customers, or prospective customers) prefer eCommerce these days. They have learnt the comfort of buying from their home, without having to take their car or the public transport to personally go and buy at each store. As always, each option has its advantages and disadvantages, but eCommerce has become a common trend now.
