When building a conversational system, how you handle off topics tends to swing one way or the other for people new to it.

They either totally forget about handling chit-chat/off topic. This leads to the system feeling stupid to the end user, even if it has a high accuracy. Or feeling like a search engine.

Or they go the other way and try to answer everything they can possibly think of, without realising the rabbit hole they are going down. Also they may only put in one response to the same type of question, making it feel like a computer program.

The correct way to handle off topics is: Answer the topic not the question, and drive the user back to the intended usage of the conversational system! 

To give an example:

User: Hey Watson, do you love Siri?
Watson: I have never really thought about other AI systems. I am too busy selling goods for pets.

User: Please tell me if you love Siri.
Watson: I would much prefer to help you in buying things for your pet, rather then talking about Artificial intelligences.

User: But do you love Siri?
Watson: Can we please get back to buying things for your pet.

The first response detects that the person is talking about an “AI” related item. But instead of trying to expend time on the nature of the question, you can deflect it with a broad response.

The second response is the same but a bit cooler, and pushing the user back to the topic.The last response stops playing with the user.

You can achieve this quite easily with the following advanced output.

{
  "output": {
    "text": {
      "values": [
       "I have never really thought about other AI systems. I am too busy selling goods for pets.",
       "I would much prefer to help you in buying things for your pet, rather then talking about Artificial intelligences.",
       "Can we please get back to buying things for your pet."
      ],
      "selection_policy": "sequential"
    }
  }
}

Someone playing with the system however will have it return to the first response on the 4th try. So you can set a counter that say after X number of off topic responses, then have it either redirect to a person, or stop responding completely.

The detection part is tricker. One way to approach this is to have a single intent that detects off topic, and then use a second workspace to drill down to the related off topic. This prevents polluting your main intents, and only requires further calls when needed. I may go into more detail on this later.

For more information on how to handle off topic/chit chat and much more, I strongly recommend doing the Designing Conversational Solutions (Watson Conversation Service) training.

In Watson Dialog the use of counters was pretty easy. In Conversation it is as well, but not as intuitive yet. Here I am going to explore three methods of building and using a counter.

First to create a counter, you need to go to advanced mode in your node. You can then type in something like the following.

{
  "output": {
    "text": "Keep saying T1"
  },
  "context": {
    "countdown": 3
  }
}

A common mistake that people make is to forget to add the comma after the output block. Conversation will normally make the editor window red, but doesn’t give any hints if you do this. Worse still, if you don’t notice the red block then Conversation will revert that node if you attempt to test or edit another node.

After this we create a node with the following output.

Counter is at: <? context.countdown-- ?>

This tells conversation to display the counter we made, then decrement it. Within the nodes condition we can check to ensure the counter is greater than 0, if not then move to the next node to tell them you are finished. For example:

Hiding the counter

Now you may not want the end user to be able to see the counter. In which case we need to mask the executing code from being rendered. To do this we create a node with the code to execute as follows:

<? context.countdown-- ?>

You then set this node to continue from another node, linking to the output (very important!). In the second node, you put the following into the advanced output window.

{
  "output": {
    "text": {
      "append": false,
      "values": [
        "Still counting down."
      ]
  }}}

What this does is generate output “Still counting down.”, but because “append” is false, it will erase the previous output. So the user only sees this message.

Do you really need a counter?

The simple solution can be to not use a counter at all. The following code for example will simulate the previous example by just cycling through custom responses.

{
  "output": {
    "text": {
      "values": [
        "Keep saying T3.",
        "Counting down 3",
        "Counting down 2",
        "Counting down 1",
        "Counting finished!"
      ],
      "selection_policy": "sequential"
    }
  }
}

Here is the sample conversation script you can play with.

Important reminder!

If you use the counter in your conversation flow, you should be responsible for it! It is good coding practice to declare it in the conversation.

Apart from being able to test, it allows you to see where the application layer would likely set the counter. Plus if something failed at the application layer you can trap easier.

Depending on the complexity of your conversational flow, you may sometimes want the application layer to handle modifying the counter. This is fine as well, but still initialize in conversation, even if the application overwrites that initialization.

Asking a persons name in Conversation allows you to personalise responses later on. However actually getting a name from a person which looks good in personalisation is quite hard.

First thing to consider is how you ask the question. Take these examples.

1. What is your name?
2. Can I get your name?
3. How do you like to be known as?

All three of these can illicit different responses from the end user, some not the name at all. Possible responses can be.

• (1) Simon O’Doherty
• (2) Why do you want to know my name?
• (2) No
• (3) Hugh Jass

None of these answers are ideal. The first one will look silly in personalisation. The next two need to be addressed. The last one is just silly.

So at this point is very important to shape what you send to the user. Actually everything you write, especially in process flow you need to be mindful of how you shape your message.

For this example we are going to use “Hello. Before we begin, can I get your name?“. This is liable to get responses as shown above about “why” and “no”. But you will also find that people will often not even read what is being said. So you could also expect a question.

To deal with this, we can create a simple entity to look for common key terms.

The reason to use an entity over intents is that in a process flow, the scope of questions is very narrow, and we don’t want to interfere with any other intent training that is required.

We can take the following actions on each entity.

@NameOptions:why
Output: “I need your name so I can be more personalised. Can I get your name?” and ask again.

@NameOptions:question
Output: “Before you ask me a question, can I get your name?” and ask again.

 
@NameOptions:no
We don’t want to force the person to give their name. So we can customise this with the following advanced code:

{
    "output": {
        "text": "No problem, you don't need to tell me your name. Please ask me a question"
    },
    "context": {
        "name": ""
    }
}

We are setting the context variable name to blank, so that personalisation doesn’t have a name.

Condition: input.text != “”
This node is to actually load the persons name using the following advanced code:

{
  "output": {
    "text": "Hello <? input.text ?>. Please ask me a question."
  },
  "context": {
    "name": " <? input.text ?>"
  }
}

Condition: true
Output: “I didn’t quite get that.” and ask again.
We use this final node in case someone enters nothing. We just send back to ask again.

Here is the sample script for above.

Outstanding Issues

So while this will work, there are still a number of issues you need to factor in.

Too much information: What happens if they answer “My name is Simon O’Doherty”. Conversation cannot handle this, and will use the whole text .

To work around this, you can try passing the users input to AlchemyAPI and Entity extraction to get the persons name. Running on the text above gives the following output (truncated):

"entities": [
        {
            "type": "Person",
            "relevance": "0.33",
            "count": "2",
            "text": "Simon O'Doherty"
        }
    ]

There are still issues with this though, which you will find when you experiment with it. Another option is a simple UIMA dictionary which detects names. I did an example of this long ago, although using WEX or Watson Knowledge Studio may make it a lot easier.

Bad information: Apart from the joke or offensive names, allowing the user to personalise responses means your system is open to abuse. So you will need to do some sanitisation at the application layer to stop this.

Response formatting: In the example script you will see that context name is being set by ” <? context.name ?>”. However when you test, you will see the preceding space is removed. Without this, you will get some odd formatting like “Hello .” if no name is specified. You need to correct this at the application layer.

Starting off for a lot of people they get a little bit confused as to how Conversation takes it’s next action. To that end I have created this sample chart that explains it all.

Here is a sample conversation project which is based on above.

So I use a number of applications to help in building and testing Watson. But the main ones I use are as follows.

• Ultraedit
• SPSS Modeller
• Excel
• Languages: Java and Python
• iPad (no seriously :))

I want to touch on UltraEdit for this one. I primarily use it for the following.

• Making mass fixes to data that SPSS can’t easily handle, and languages take too long.
• Making the JSON readable
• Extracting meaningful data from JSON to work with.
• Putting that data back easily.

It is a pretty powerful text editor which allows you to do very complex search/replace/modifications to data. I am of the opinion if you prefer a different text editor, then go with what you know. But here are the main scripts I use for UltraEdit.

When you open the conversation JSON file it will look a little bit like this.

Just a mess. There is two scripts I picked up from UltraEdit that allow you to decompress it and compress it back to what you see above.

JSON – readable

Will turn it into this.

To turn it back again.

JSON – compress

Now there are purists out there that can live with reading JSON. I’m sure all they can see is blond, brunette, redheads. For me I prefer to have my data with a lot less noise.

To that end I created two macros for extracting the entities and intents from the conversation file, into a format you can open in excel/SPSS.

Conversation – get intents

Creates a CSV file you can work with.

Conversation – get entities

This one creates a Tabbed separated file (TSV) with the entities in a nice readable format.

I also have scripts to put the intents and entities back into the conversation script, but I don’t plan to release at this time. As it is so easy to make a mess, and I won’t be supporting these.

You can pick up the scripts here.

So I will be switching between, newbie to expert in no particular order. This post assumes you already know how to use conversation.

In earlier versions of Watson, it was designed to handle high, medium and low confidence answers in different ways. With conversation this all works a little bit differently.

For this example, I am using the NLC demo intents. The first thing you have to do is find your low confidence.

Unlike earlier versions of WEA, the confidence is relative to the number of intents you have. So the quickest way to find the lowest confidence is to send a really ambiguous word.

These are the results I get for determining temperature or conditions.

treehouse = conditions / 0.5940327076534431
goldfish = conditions / 0.5940327076534431
music = conditions / 0.5940327076534431

See a pattern? 🙂 So the low confidence level I will set at 0.6. Next is to determine the higher confidence range. You can do this by mixing intents within the same question text. It may take a few goes to get a reasonable result.

These are results from trying this (C = Conditions, T = Temperature).

hot rain = T/0.7710267712183176, C/0.22897322878168241
windy desert = C/0.8597747113239446, T/0.14022528867605547
ice wind = C/0.5940327076534431, T/0.405967292346557

I purposely left out high confidence ones. In this I am going to go with 0.8 as the high confidence level.

Once you have those, you can create your conversation. Your first node is to check that later nodes won’t fail.

The next nodes check to see if a low confidence or medium confidence is hit. For low, it won’t respond. For medium, it will display text, then continue from the next condition to find the actual answer.

When you test it, you get the following results.

You may still need to test with users to tweak the upper value.

Using this fall through method makes it easier to maintain with your intents. You can use a nested option if only certain intents need to be hit.

Here is the Sample file.

Gotchas: When writing numbers in conditions, always start it with a numerical value like “0.8”, not “.8”. Otherwise you will get a Dialog node error: EL1049E:(pos 24): Unexpected data after ‘.’: ‘8’