Contextual Conversations

Contextual Conversations

In a contextual conversation, something beyond the previous step in the conversation plays a role in what should happen next. For example, if a user asks "How many?", it's not clear from the message alone what the user is asking about. In the context of the assistant saying, "You've got mail!", the response could be "You have five letters in your mailbox". In the context of a conversation about outstanding bills, the response could be, "You have three overdue bills". The assistant needs to know the previous action to choose the next action.

To create a context-aware conversational assistant, you need to define how the conversation history affects the next response.

Example Conversations

User Likes Music

User Doesn't Like Music

Step-by-step Guide on Creating Contextual Conversation Patterns

1. Defining Slots

Slots are your assistant's memory. Slots store pieces of information that your assistant needs to refer to later and can direct the flow of the conversation based on slot_was_set events. There are different types of slots, and each affects the conversation flow in its own way.

In the concert bot example, the likes_music slot is a boolean slot. If it is true, the bot sends an intro message. If it is false, the bot sends a different message.

domain.yml
slots:
  likes_music:
    type: bool
    mappings:
      - type: custom

2. Creating Stories

Stories are examples of how conversations should go. In the example above, the concert bot responds differently for users who like music and users who don't because of these two stories:

stories.yml
stories:
  - story: User likes music
    steps:
      - intent: how_to_get_started
      - action: utter_get_started
      - intent: affirm
      - action: action_set_music_preference
      - slot_was_set:
          - likes_music: True
      - action: utter_awesome

- story: User doesn't like music
    steps:
      - intent: how_to_get_started
      - action: utter_get_started
      - intent: deny
      - action: action_set_music_preference
      - slot_was_set:
          - likes_music: False
      - action: utter_goodbye

3. Configuring the TEDPolicy

In addition to adding stories to account for context, machine learning policies can help your model generalize to unseen conversation paths. It is important to understand that using machine-learning policies does not mean letting go of control over your assistant. If a rule-based policy is able to make a prediction, that prediction will always have a higher policy priority and will predict the next action.

The TEDPolicy is made to handle unexpected user behaviors. For example:

stories:
  - story: Story from conversation with a2baab6c83054bfaa8d598459c659d2a on November 28th 2019
    steps:
      - checkpoint: STORY_START
      - intent: greet
      - action: action_greet_user
      - slot_was_set:
          - shown_privacy: true
      - intent: ask_whoisit
      - action: action_chitchat
      - intent: ask_whatspossible
      - action: action_chitchat
      - intent: telljoke
      - action: action_chitchat
      - intent: how_to_get_started
      - entities:
          - product: enterprise
      - slot_was_set:
          - product: enterprise
      - action: utter_explain_enterprise
      - action: utter_also_explain_nlucore
      - intent: affirm
      - action: utter_explain_nlu
      - action: utter_explain_core
      - action: utter_direct_to_step2

Setting max_history

Usually, only a certain amount of context is relevant to your assistant. max_history is a hyperparameter for Rasa dialogue management policies that controls how many steps in a dialogue the model looks at to decide which action to take next.

In the story below, the user asks for help three times in a row. The first two times, the bot sends the same message, but the third time, it hands them off to a human:

stories.yml
stories:
  - story: user persists in asking for help
    steps:
      - intent: help
      - action: utter_help
      - intent: help
      - action: utter_help
      - intent: help
      - action: action_human_handoff

You can set the max_history by passing it to your policy's settings in your config file, for example:

config.yml
policies:
  - name: "TEDPolicy"
    max_history: 5

Summary

Here's a summary of the concepts you can apply to enable your assistant to have contextual conversations: