Introduction to Dialogue Systems

User Input System Output ?

User Input System Output Understand Input Generate Output ?

User Input System Output 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 adjective I verb phrase hungry(user)

User Input System Output Create Semantic Representation Generate Text from a Semantic Representation Dialogue Manager

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