By the end of this section, you will learn how you can train a bot to understand a User’s message so that the bot can reply back with an appropriate response.
What is User Says?
Whenever a user sends a message, we try to understand what the User is trying to say using various Machine Learning algorithms and find the corresponding node on mogambo. One such set of algorithms that we use at Haptik is the Sentence Similarity module, which is a suite of many different ML algorithms that help in finding the right node.
For example, if you want a node to get detected when a user types * “Status of my complaint”*, then you should add *“Status of my complaint”
- as a User Says on that node.
Tip: Add multiple variations of the response to make the Bot smarter and understand more variations. For Example: “Status of my complaint”, “Complaint status”, “I want to know about my complaint” etc.
Depending on how and when you want your node to get detected, there are 3 types of User Responses that can be configured on a node:
- Independent Responses
- Dependent Responses
- Negative Responses
If you created a Start Node to get detected at the beginning of a conversation, then you would add the corresponding user responses for the node under this category.
Best Practices: Use independent responses only on a Start Node
For example, in a lead gen bot as below, we would add the following User Responses on the “Show Services” node and “About Demat Account”.
|Show Services||About Demat Account|
|What products do you offer||What is a demat account|
|Can you show me the services you offer||Can you help me with info about demat account|
|Show services||Tell me about demat account|
Tip: User Responses are case insensitive and ignore punctuations. So don’t and dONt are the same.
If you have connecting nodes, and you want the child node to be detected only when when the user says something on the parent node, then you can use Dependent Responses.
For example, after asking for Demat Account, if the User wants to know how to open an account, you could add "Open Account" as a Dependent User Response on "Open Demat Account" with "Demat".
The above sections explained how you can select a node depending on what the user said. This section will explain how to filter out nodes depending on what the user says.
For example, if you have the following nodes:
|Good Service||Bad Service|
|Your service was very good||Your service was not good.|
|Your service was excellent||Your service was bad|
Now when a user says "Your service was not good", even though the User meant something very different, because of high similarity of the sentences in both the nodes, it is possible that an Incorrect node may get selected.
To avoid this, you can add specific keywords under Negative Responses of the node which will filter out the node, if the keyword is a part of the user's message.
For Example, in the above scenario, if we add "bad" as a Negative Response on the "Good Service" node, then the Good Service node will not be considered if the User's message has "bad" in it's sentence.
Common Mistakes: Adding similar responses on 2 different nodes
Sometimes, you might have nodes which have sentences like “I want to buy this product”, “I want to purchase this product” etc. on a Shopping Node.
If you notice these responses, the only difference is “buy” and “purchase” which are words with the same meanings i.e. they are synonyms.
To simplify and reduce the effort of generating such User Responses where a common set of words with same meaning are needed, you can enable synonyms for specific words in the bot.
Tip: Black Underline means synonyms are available. Blue underline means Synonyms for that word have been enabled in the Bot.
For words which have Synonyms, you can double click on the word and select “View Synonyms” from the menu.
This will open up a list of all available Synonyms for the word you selected.
After viewing the synonyms, just select the "Enable" checkbox in the View Synonyms modal.
So for our Shopping Node example, if you Double click on “buy” -> Select “Add Synonyms” from the context menu -> Enable a specific Synonym cluster, then you can avoid adding variations of the word "buy".
Common Mistake: Ensure that you add the correct set of synonyms as 1 word can have multiple meanigs. For example, “speaker” could mean the electronic devices as well as a human speaking at an event.
To account for user’s mistakes, the Sentence Similarity module accounts for some spelling mistakes that a user could make. For example, if a user types “Show me servises” or “Show me serveces”, they would still be matched to the “Show Services” node as per the above examples.
The User Responses added on the node above are processed using the Sentence Similarity Module to generate the machine learning models. These models are then used on User’s every message to select the node which matches the requirements.
To generate these models, you will have to click on Train Bot. This will send an email with the result of the training process. It will also contain details of any probable errors which could come up because of incorrectly entered data.
For example, if you add very similar sentences in two different nodes, then the resulting email will show details about the conflicting nodes and responses.
Common Mistakes: Bot Builders often forget to click on Train Bot after making changes in the User Says of a node.
Tips: Check email for conflicting responses or other errors in the User Responses in the bot.