Technology plays certainly a role, but the most significant performance gains are obtained by developing a good understanding of the fundamental NLU concepts.
An intent captures the general meaning of a sentence (or an utterance in the chatbots lingo). For example, the sentences below convey the intent of being hungry, let’s call it
- I am hungry
- I need to eat something
- I am starving
- My kingdom for a pizza
How do we teach our model that these utterances convey the
i_am_hungry intent? We train it to distinguish them from sentences with other meanings. We create a dataset containing examples of different intents.
Here is a sample of a dataset in Botfront where you can see examples for 2 different intents. This project has more than 150 in total.
We said that intents carry the meaning of a sentence. How does a program understand meaning? Let’s just say that there’s a way to express the meaning of words with numbers (or vectors). The long explanation is here if you’re interested, but the essential idea is that vectors can be compared (a distance can be calculated), and that a small distance indicates the words have similar meaning.
In Rasa, the Spacy pipeline comes with ready-to-use pretrained vectors, while the Tensorflow pipeline will train its own vectors on your dataset. The latter implies that you will need more examples, but your reward is that it will be more accurate on your custom or domain vocabulary, and more resilient to spelling mistakes.
Only the Tensorflow Pipeline is supported in Botfront at this time.
Usually, an intent carries an action or an expectation.
Entities are positional elements in an utterance. In the following examples, we’re trying to build a currency exchange assistant. To provide a rate, we need the know which currency the user wants to buy, and which currency they want to sell.
We have thus 2 entities:
currency_sell, and they can have any currency as value. It is important to keep the entity name as generic as possible.
A common mistake is to choose the entity name as the value like this:
Finally, it should be noted that recognizing the intent and extracting entities are two separate tasks: in other words, having similar entities in utterances will not influence the model when it parses it to recognize the intent.