I have something I need to confess. I have a personal hatred of Entities. At least in their current form.

There is a difference between deterministic and probabilisitic programming, that a lot of developers new to Watson find it hard to switch to. Entities bring them back to that warm place of normal development.

For example, you are tasked with creating a learning system for selling Cats, Dogs, and Fishes. Collecting questions you get this:

• I want to get a kitten
• I want to buy a cat
• Can I get a calico cat?
• I want to get a siamese cat
• Please may I have a kitty?
• My wife loves kittens. I want to get her one as a present.
• I want to buy a dog
• Can I get a puppy?
• I would like to purchase a puppy
• Please may I have a dog?
• Sell me a puppy
• I would love to get a hound for my wife.
• I want to buy a fish
• Can I get a fish?
• I want to purchase some fishes
• I love fishies
• I want a goldfish

The first instinct is to create a single intent of #PURCHASE_ANIMAL and the create entities for the cats, dogs and fishes. Because it’s easier to wrap your head around entities, then it is to wonder how Watson will respond.

So you end up with something like this:

Wow! So easy! Let’s set up our dialog. To make it easier, lets use a slot.

In under a minute, I have created a system that can help someone pick an animal to buy. You even test it and it works perfectly.

IT’S A TRAP!

First the biggest red flag with this is you have now turned your conversation into a deterministic system.

Still doing cross validation to test your intents? Give up, it’s pointless.

You can break it by just typing something like “I want to buy a bulldog”. You are stuck into an endless loop.

The easiest solution is to tell the person what to type, or link/button it. But it doesn’t exhibit intelligence (and I hate buttons more than I hate entities 🙂 ).

The other option is to add “bulldog” to the @Animals:Dog entity. But when you go down that rabbit hole, you could realistically add the following.

• 500+ types of breeds.
• Common misspellings of those words.
• plurals of each breed.
• slang, variations and nicknames of those animals.

You are easily into the thousands of keywords to match, and all it takes is one person to make a typo you don’t have in the list and it still won’t work.

Using entities in a probabilistic way.

All is not lost! You can still use entities, and keep your system intelligent. First we break up the intents into the types of animals like so:

So now if I type “I want to buy a bulldog” I get #PurchaseDog with 68% confidence. Which is great, as I didn’t even train it on that word.

So next I try “I want to buy a pet” and I get #PurchaseCat with 55% confidence.

Hmm, great for cat lovers but we want conversation to be not so sure about this.

So we create the entities as before for Cat, Dog, Fish. You can use the same values.

Next before you check intents, add a node with the following condition.

This basically ensures that irrelevant hasn’t been hit, and then checks if the animal entities have not been mentioned.

Then in your JSON response you add the following code.

{
    "context": {
    "adjust_confidence": "<? intents[0].confidence = intents[0].confidence - 0.36 ?>"
    },
    "output": {
        "text": {
            "values": [],
            "selection_policy": "sequential"
        }
    }
}

The important part is the “adjust_confidence” context variable. This will lower the first intents confidence by 0.36 (36%).

We set the node to jump to the next node in line, so it can check the intents.

Now we get “I don’t understand” for the pet question. Bulldog still works as it doesn’t fall below the 20%.

Demo Details.

I used 36% for the demo, but this will vary in other projects. Also if your confidence level is too high, you can pick a smaller value, and then have another check for a lower bound. In other words, set your conversation to ignore any intent with a confidence lower then 30%, and then set your adjustment confidence to -10%.

Advantages

Using this approach, you don’t need to worry as much with training your entities, only your intents. This allows you to build a probabilistic model which isn’t impacted unless it is unsure to begin with.

I have supplied a Sample Conversation which demos above.