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Towards A Context-Based Dialog Management Layer for Expert Systems
Victor Hung, Avelino Gonzalez & Ronald DeMaraIntelligent Systems Laboratory
University of Central Florida
eKNOW ConferenceFebruary 4, 2008
University of Central Florida www.ucf.edu
Agenda
Introduction Background Approach Evaluation Project LifeLike
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Expert System
Introduction
Interface KnowledgeBase
User
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Introduction
Focus on Interface Component Text-based Conversation-based
Idealistic view of Human Computer Interaction Media and science fiction expectations Open, unconstrained dialog Natural conversational flow
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Background Natural Language Processing (NLP)
Relevant techniques Not primary focus
Dialog System Design General issue at hand
Context-Based Reasoning (CxBR) Main architectural method
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Natural Language Processing
Major issues in NLP (Wilks, 2005) Linguistic Systems
Resolve ambiguities (ASR, syntactic, semantic) Knowledge Representation
Relationship of language and logic Information Corpora
Ontologies, tree banks, Semantic Web project Statistical and Quantitative Methods
Machine learning techniques on linguistic data
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Dialog System Design
Dialog Manager
Dialog History
Interpreter
Generator
Dialog Model System Task Model
Domain KnowledgeManager
Domain Task Model
Knowledge Base 1
Knowledge Base 2
Knowledge Base 3
Flycht-Eriksson and Jönsson, 2000
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Chatbot Systems Conversation Agents
General chatting techniques ELIZA (Weizenbaum, 1966) Persona-AIML (Galvão et al, 2004) Genericity (Sansonnet et al, 2006)
Embodied Conversation Agents Sam (Cassell et al, 2000) Laura (Bickmore and Picard, 2004) Mel (Lee et al, 2005) Sergeant Blackwell (Traum, 2006)
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Dialog Systems
Open issues Evaluation process is too subjective No tremendous breakthroughs since ELIZA Naturalness still a barrier Scarce work on context-based dialog
management Cognitive model-based systems require
extensive expertise
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Context-Based Reasoning
Contexts Context-Transition Logic Missions Agent Interface
Context Topology
Mission
Context C0
Context C4
Context C1
Context C3
Context C2
Agent Interface
EnvironmentData
Agent Action
Inference Engine
Stensrud et al, 2004
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Context-Based Methods in NLP
Situational contexts used for general NLP tasks Speech recognition
Fügen et al, 2004 Sarma and Palmer, 2004 Serridge, 1997 Young, 1989
Context-based ML (Lieberman et al, 2005)
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Context-Based Methods in NLP
Open issues Context-based methods treated as a
gimmicky technique Still at a state of infancy
Use of context heavily prevalent for NLP ambiguity resolution tasks
Dialog management applications not fully explored
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Approach
Goal Management Allow for non-linear conversation flow Support for goals
Asynchronous Multiple
Illusion of open dialog Limited to an expert domain, and the user is
cognizant of the dialog system’s functionality as an expert entity
User’s goals are limited to those related to the chatbot’s expertise
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Contexts In Goal Management
Context Internal and external circumstances
Goal Desired end state
CxBR Context-goal relationships
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Knowledge In Goal Management
ContextualizedKnowledge Base
User Data Base
General KnowledgeBase
Domain-SpecificKnowledge Base
Contextualized KnowledgeContext
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Framework for Goal Management
Goal Recognition NLP-based context
activation Goal Bookkeeping
Discourse Goal Stack Model (Branting et al, 2004)
Context Topology Agent Goals User Goals
Context Topology
Mission Goals
Context C0
Context C4
Context C1
Context C3Context C2
Disambiguated Utterance
Inference Engine
Contextualized Knowledge
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Evaluation
Plagued by subjectiveness Measuring Naturalness
PARADISE (Walker et al, 1997) User questionnaire (Semeraro et al, 2003)
(Rzepka et al, 2005) Universal chatbot evaluation system
(Shawar and Atwell, 2007)
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Project LifeLike
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KnowledgeManager
Speech Disambiguator
Context-based Dialog Manager
LifeLike Dialog System
LifeLikeRecognizer NSF User
Data
GeneralKnowledge
AskAlexOntology
Spell Check
Semantics Check
ContextSpecific
Knowledge
LifeLikeSpeechOutput
Dictation String
Phrase String
ContextC
on
text
Disa
mb
igu
ate
dS
tring
Context
Dataset
Context
Dataset
Response String
Response String
Updated Data
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Questions