Introduction to Dialogue Systems

UserInput

SystemOutput

?

UserInput

SystemOutput

Understand Input

Generate Output

?

UserInput

SystemOutput

Generate Text from a Semantic Representation

?

Create Semantic Representation

Semantic Representation

• No transformation

• Text categorization

• Parse

• Predicate Logic form

I am hungry.

FOOD_REQUEST

am hungry

noun

verb adjectiveI

verb phrase

hungry(user)

UserInput

SystemOutput

Create Semantic Representation

Generate Text from a Semantic Representation

DialogueManager

Dialogue Managers

• The Dialogue Manager integrates information from user to update an internal dialogue model

• Using this model, the Dialogue Manager can evaluate different courses of actions and choose one

Language Ambiguity

• Language is incredibly ambiguous “I saw the man with a telescope.”

• Dialogue Managers use knowledge to reduce ambiguity and find out what is really intended from what is spoken– Do I have a telescope?– Is there a telescope on the man?

Dialogue Models are the Core

• Keeps track of context– The goals of participants– Focus of the conversation

• Gives meaning to the conversation

Dialogue Models : Reference

“Jim took a picture of my turtle. He gave it to me.”

= Jim = the turtle

Dialogue Models : Initiative

• Fixed Single Initiative– 1) Computer asks all of the questions, and

human answers– 2) Human makes repeated requests

• Mixed Initiative– More flexible trade-off between computer

requests and human input

Dialogue Managers : Explicit Correction

“I’d like to bill a call to my credit card.”“Ok, the call will be $20.”

“Actually, could I call collect instead?”

Dialogue Managers : Task Complexity

Placing a phone cal

Train routing

Simple

Hierarchical/Complex