You must have heard a lot about chatbots already. Maybe you read this article in Forbes or this one in Business Insider, or you have realized chatbots became a hot topic after attending a business conference. Whatever the reason, you know a chatbot is a must in today's dynamic business environment. Let´s imagine you have decided to create one adapted to your needs, using the Microsoft Bot Framework, one of the most popular software development kits. You want your bot to be a chatbot, and therefore you use LUIS, Language Understanding Intelligent Service, so that your bot can make sense of what people say.
So you create a new App…
…add an intent or a couple of intents (types of requests you would like your chatbot to analyze)…
…then you add entities, then features, then utterances (the sentences users would typically put)…
And then you should tag them.
Then you train your application to get even more entities, enter more utterances, etc. and yes, you may do it also via API, but you need to tag the input anyway.
Finally, you put your application to the test and…
What?! Why does it think soda is a kind of pizza rather than a drink?
Well, it’s just not ready yet, but practice makes perfect. It will take some more time and effort. You will need to provide much more input and, last but not least, increase the complexity of sentences.
It comes as no surprise that at this point you are actively seeking solutions that would make all this process more straightforward and less time-consuming. At Bitext, we apply extensive linguistic resources and knowledge to deal with problems often encountered in a standard chatbot training. We have mentioned some of them in our recent posts, here I will focus on their practical application in developing a chatbot:
- Morphological analysis and lemmatization allow for efficient recognition of entities, be they plural or inflected nouns; once an entity is introduced, all its forms are recognized by a chatbot.
- Sentence rewriting makes the training faster, as different sentences are reformulated into more simple ones; dealing with a reduced number of phrases the chatbot is “learning” faster.
- Parsing shows relationships in the sentence, helping to tackle such issues as a double intent or coordination.
Natural Language Processing Framework, efficient and exhaustive solution offered by Bitext, can be implemented as a middleware adapted to work with different AI development platforms. Using it you may train your chatbot yourself faster and with better results, getting the most out of it, while keeping to yourself the data sets you use for the training.
See how it works by clicking here!