Monthly Archives: January 2017

Improving your Intents with Entities.

You might notice that when you update your entities that Conversation says “Watson is training on your recent changes”. What is happening is that Intents and Entities work together in the NLU engine.

So it is possible to build entities that can be referenced within your intents. Something similar to how Dialog entities. Work.

For this example I am going to use two entities.

  • ENTITY_FOODSTUFF
  • ENTITY_PETS

In my training questions I create the following example.

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The #FoodStore question list is exactly the same, only the entity name is changed.

Next up create your entities. It doesn’t matter what the entity itself is called, only that if has one value that mentions the entity identifiers above. I have @Entity set to the same as the value for clarity.

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“What is the point?” you might ask? Well you will notice that both entities have a value of “fish”.

When I ask “I want to get a fish” I get the following back.

  • FoodStore confidence: 0.5947581078492985
  • Petshop confidence: 0.4052418921507014

So Watson is not sure, as both intents could be the right answer. This is what you would expect.

Now after we delete the “fish” value from both entities, I then add the same training question “I want a fish” to both intents. After Watson has trained and I ask “I want to get a fish”, you get the following back.

  • Petshop confidence: 0.9754140796608233
  • FoodStore confidence: 0.02458592033917674

Oh dear, now it appears to be more confident then it should be. So entities can help in making questions ambiguous if training is not helping.

This is not without it’s limitations.

Entities are fixed keywords, and the intents will treat them as such. So while it will find “fish” in our example, it won’t recognise “fishes” unless it’s explicitly stated in the entity.

Another thing to be wary of is that all entities are used in the intents. So if a question mentioned “toast”, then @ENTITY_FOODSTUFF becomes a candidate in trying to determine which intent is correct.

The last thing to be aware of is that training questions take priority over entities when it comes to determining what is correct.

If we were to add a training question “I want fishes” to the first example. Then ask the earlier question, you would find that foodstore now takes priority. If we add “I want fishes” to both intents and ask the question “I want to get a fish”, you will get the same results as if the entities never had the word “fish” in it.

This can be handy for forcing common spelling mistakes that may not be picked up, or clearly defined domain keywords a user may enter (eg. product ID)

 

Prioritizing Intents

A common question that comes up is how to handle where the end user makes two utterances, but you only want to take action on one.

The most common being someone saying hello, versus saying hello with a question. You would want the question to take priority.

It’s very easy to do. You just do the following:

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  1. Create your first node with a condition of True and create your priority intents under this. Set your top node to jump to the first in that branch.
  2. Create your second node which handles greetings.
  3. Add a True node at the end of your important intents, and let it jump to greeting condition.

And that’s it! But that is the old style conversation way. Just before the new year a new version of conversation was released that makes this so much more simple.

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The magic is in the first node.

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Here we check to ensure that a greeting hasn’t been mentioned, then check each important intent.

With this method you don’t need any complex branches or jumping around. One important thing is to ensure that your less important intents do not have any training data that may cause it to pick it over the important intents.

Sample workspaces available.

 

 

Conversing in three dimensions.

There is one feature of Conversation that many people don’t even factor in when creating a conversational system. Let’s take the standard plan to spell it out.

  • Unlimited API queries/month
  • Up to 20 workspaces
  • Up to 2000 intents
  • Shared public cloud

Yep, you have 20 workspaces to play with! Most people starting off just use it for development, testing and production. But there is so much more. Putting it in context.

complexityofconversation

Functional Actions

For those new to Conversation, the first experience is normally the car demo. This is a good example of functional actions. In this case you know your application, and you want your end user to interact with it. So the user normally has prompts (conversational or visual) to allow them to refer to your user interface.

These offer the least resistance of building. The user is taught the names of the interfaces by using it, and are unlikely to deviate from that language. In fact if they do use non-Domain language, it is more likely a fault of your user interface.

Question & Answers

This is where you have collected questions from the end user, to determine what the answer/action that is needed to be taken.

Often the end user does not understand the domain language of the documentation or business. So training on their language helps making the system better for them.

Process Flows

This is where you need to converse with the user, to collect more information to drive to meeting their needs.

Multiple Workspaces

Most see this as just creating a workspace for development, testing and production. But using these as part of your overall architecture can dramatically increase the functionality and accuracy of the system.

Two main patterns that have been used are off-topic + drill down.

Off-Topic / Chit chat.

In this model we have a primary workspace which stores the main intents, as well as an intent for off-topic + chit-chat. Once one of these is detected, a second call is made out to the related workspace.

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From a price point of view this works well if you are calling out to a subject matter that is asked infrequently. If the user will often ask these questions though, then the drill down method is a better solution.

Drill Down.

This model is where the user asks a question which has a more expanded field going forward. For example when you enter a bank you may ask the information desk about mortgages, who will direct you to another desk to go into more detail on your questions.

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For this to work well, you need clear separation of what each workspace does. So that an off topic is triggered so as to pass back to the main workspace.

When planning your model look for common processes vs entities. The example above might not be good to separate by pets, as they will share common questions with a different entity. But you could separate between purchasing, accessories, etc.

As long as the conversation will not switch topics often then costs are kept down.

Multiple Workspace Calls.

This is not a recommended model as your costs go way up. It was originally used when there was a 500 intent limit per space (NLC).

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If money is no object, then this model works where you may have more then 2000 intents, or a number of intents that share similar patterns but you need to distinguish between them.

You need to factor in if your conversation service is returning relative or absolute confidences. If relative, then responses are relative to their workspace and not to each other.

If you do have numerous intents, it may be easier and better to use a different solution, like Discovery Service for example.