Chitchat and FAQs

FAQs and chitchat are two cases where the conversational assistant responds with a fixed set of messages, and the assistant should always answer the same way, no matter what has happened previously in the conversation. For example, in the following conversation, every question can be asked at any point in the conversation, with the answer being independent of anything the user has said previously.

Step-by-step Guide on Using Response Selector for FAQs and Chitchat#

To handle FAQs and chitchat you'll need a rule-based dialogue management policy (the RulePolicy) and an easy way to return the appropriate response for a question (the ResponseSelector).

1. Updating the configuration#

For FAQs and chitchat, you always want the assistant to respond the same way every time the same type of question is asked. Rules allow you to do exactly that. To use rules, the you need to add the RulePolicy to your policies in your configuration file:

Next, include the ResponseSelector in your NLU pipeline in your configuration file. The ResponseSelector requires a featurizer and intent classifier to work, so it should come after these components in your pipeline, for example:

By default, the ResponseSelector will build a single retrieval model for all retrieval intents. To retrieve responses for FAQs and chitchat separately, use multiple ResponseSelector components and specify the retrieval_intent key:

2. Defining Retrieval Intents and the ResponseSelector#

Consider an example where you have 20 different FAQs. Although each question is represented as an individual intent, all FAQ intents are handled the same way in the dialogue. For each FAQ intent, the assistant retrieves the proper response depending on which question has been asked.

Instead of writing 20 rules, you can use a single action, e.g. utter_faq to handle all FAQs with a single rule by grouping them together under a single retrieval intent called e.g. faq.

The single action uses the output of the ResponseSelector to return the correct response for the specific FAQ that the user asked.

3. Creating rules#

You need to write only one rule for each retrieval intent. All intents grouped under that retrieval intent will then be handled the same way. The action name starts with utter_ and ends with the retrieval intent's name. Write rules for responding to FAQs and chitchat:

The actions utter_faq and utter_chitchat will use the ResponseSelector's prediction to return the actual response message.

4. Updating the NLU Training Data#

NLU training examples for the ResponseSelector look the same as regular training examples, except that their names must refer to the retrieval intent they are grouped under:

Be sure to update your domain file to include the added chitchat intent:

5. Defining the responses#

Responses for the ResponseSelector follow the same naming convention as retrieval intents. Besides this, they can have all the characteristics of normal bot response. For the chitchat intents listed above, our responses could look like:

Summary#

Once you've done the following, you can train your bot and try it out!

• Add RulePolicy to your policies and ResponseSelector to your pipeline in config.yml
• Add at least one rule for responding to FAQs/chitchat
• Add examples for your FAQs/chitchat intents
• Add responses for your FAQs/chitchat intents
• Update the intents in your domain

Now, your assistant should be able to respond correctly and consistently to FAQs or chitchat, even if these interjections happen while your assistant is helping the user with another task.