A Tale of Two Dialogue Models


1
A Tale of Two Dialogue Models
CISC882 Natural Language Processing
Final Project

• By Sachin Kamboj

2
The Two Models

• Missing Axiom Theory Model
• Circuit Fix-It
• Ronnie W. Smith D. Richard Hipp Alan W.
  Biermann An architecture for voice dialog
  systems based on prolog style theorem proving
  Computational Linguistics Volume 21 Number 3
  September 1995.
• Multimodal Speech and Gesture Models
• Oviatt et. al. Designing the user interface for
  multimodal speech and pen-based gesture
  applications State of the art systems and future
  research directions Human Computer Interaction
  2000 vol. 15(4) pg 263-322

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Missing Axiom Theory Model
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Circuit Fix-ItAims/Target Behavior

• The system should be able to handle
• Convergence to a goal
• Subdialogues and effective movement between them
• An accounting of user knowledge and abilities
• Change of initiative
• Expectation of user input

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Circuit Fix-ItSample Dialogs

• Computer Put the knob to one zero.
• User Okay.
• Computer What is the voltage between connector
  one two one and connector three four
  
• User I do not know.
• Computer Locate the voltmeter.
• User Done.
• Computer Add a wire between the minus com hole
  on the voltmeter and the
  
• connector one two one.
• User How do I do that
• Computer Connect the end of the black wire
  with the small plug to the minus
  
• corn hole on the voltmeter.
• User Done.
• Computer Connect the end of the black wire with
  the large plug to connector one
  
• two one.
• User Which Knob
• Computer Near the top boundary and in the
  center is a green region. Now in the middle
  right of this green region is the control knob.
  
• User Okay.
• Computer Put the knob to one zero.

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Circuit Fix-ItProlog Style Theorem Proving

• Based on the Missing Axiom Theory
• System is built around a theorem prover and the
  role of language is to supply the missing axioms
  
• Goal is stated as a prolog axiom to be proven
• The system tries to prove the axiom in a top-down
  fashion.
  
• If the proof succeeds using internally available
  knowledge the dialog terminates without any
  interaction with the user.
  
• If the proof fails the system tries to find the
  missing axiom by engaging in a dialog
  
• observeposition(sw1X) find(sw1)
  reportposition (sw1X)

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Circuit Fix-ItImplementing the Subdialog Feature

• One of the requirements of the system is to allow
  subdialogs.
  
• As the system engages in conversation only to
  prove missing axioms each subdialog involves a
  separate proof.
  
• Hence the system cannot follow a simple
  depth-first policy to complete a proof.
  
• Instead to switch between subdialogs the system
  should allow the freezing of any proof and the
  transfer of control to a different proof
  
• Partially completed proofs have to be maintained
  in memory.
  
• Freezing of proofs handled through an
  Interruptible Prolog Simulator (IPSIM)

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Circuit Fix-ItAccounting for User Knowledge

• The system should know what the user is capable
  of doing
  
• The requests should match the abilities of the
  user
  
• Abilities of different users will vary
• Novice users will know how to adjust a knob but
  may not know how to take a voltmeter reading
  
• The system uses a user model to determine what
  can be expected of the user.
  
• The users capabilities are specified in the form
  of prolog style rules
  
• If the input describes some physical state then
  conclude that the user knows how to observe the
  physical state. In addition if the physical state
  is a property then infer that the user knows how
  to locate the object that has that property.

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Circuit Fix-ItMechanisms for Obtaining Variable
Initiative

• Variable initiative takes a role in selecting the
  next subdialog to be entered.
  
• The system implements four levels of initiative
• Directive Mode unless the user needs
  clarification the system selects its response
  according to the next goal
  
• Suggestive Mode the system will select its
  response depending on the next goal but will
  allow interruptions to subdialogs about related
  goals
• Declarative Mode the user has dialog control
  but the system is free to mention relevant facts
  
• Passive Mode The user has complete control. The
  system will provide information only in direct
  response to the users questions.

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Circuit Fix-ItImplementation and Uses of
Expectation

• If the computer produces an utterance that is an
  attempt to have a specific task step S performed
  there are expectations for any of the following
  types of responses
• A statement about the missing or uncertain
  background knowledge necessary for the
  accomplishment of S
  
• A statement about a subgoal of S.
• A statement about the underlying purpose for S.
• A statement about ancestor task steps of which
  accomplishment of S is a part
  
• A statement indicating the accomplishment of S.
• Expectations serve two purposes
• The detection and correction of errors
• Provide an indication of the shift between
  subdialogs

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Circuit Fix-ItImplementation and Uses of
Expectation

• The system computes the expectations and the cost
  of each expectation.
  
• The system also computes a set of meanings (or
  semantic representations) of user utterances with
  a corresponding cost
  
• The system combines the two costs
• C ß µ (1 ß)E
• The meaning with the smallest total cost is
  selected as the output of the parser

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Circuit Fix-ItImplementation and Uses of
Expectation

• An important side effect of matching meanings
  with expectations is the ability to interpret an
  utterance whose content does not specify its
  meaning.
• The reference of pronouns
• Turn the switch up
• Where is it
• The meaning of short answers
• Turn the switch up
• Okay
• Maintaining dialog coherence

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Basic Algorithm

• ZmodSubdialog(Goal)
• Create subdialog data structures
• While there are rules available which may achieve
  Goal
  
• Grab next available rule R from knowledge unify
  with Goal
  
• If R trivially satisfies Goal return with
  success
  
• If R is vocalize(X) then
• Execute verbal output X (mode)
• Record expectation
• Receive response (mode)
• Record implicit and explicit meanings for
  response
  
• Transfer control depending on which expected
  response was received
  
• Success response Return with success
• Negative response No action
• Confused response Modify rule for
  clarification prioritize for execution
  
• Interrupt Match response to expected response
  of another subdialog
  
• Go to that subdialog (mode)
• If R is a general rule then
• Store its antecedents
• While there are more antecedents to process

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Multimodal Speech and Gesture Interface Models
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Introduction

• What are multimodal interfaces
• Humans perceive the world through senses
• Ears (hearing) Eyes (sight) Nose (smell) Skin
  (touch) and Tongue (taste)
  
• Communication through one sense is known as a
  mode
  
• Computers may process information through modes
  as well
  
• Keyboards Microphone Mice etc.
• Multimodal interfaces try to combine two
  different modes of communicating.
  
• borrowed from a talk on Multimodal
  Interfaces by Joe Caloza

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Advantages

• Combination of modalities allows more powerfully
  expressive and transparent information seeking
  dialogues
  
• Different modalities provide complimentary
  capabilities
  
• Users prefer speech input for functions like
  describing objects and events and for issuing
  commands
  
• Pen input is preferred for conveying symbols
  signs and gestures and for pointing and selecting
  visible objects
  
• Multimodal pen/voice interaction can result in
  10 faster task completion time 36 fewer
  task-critical content errors 50 fewer
  spontaneous disfluencies and shorter and more
  simplified linguistic constructions
• Corresponds to a 90-100 user preference to
  interact multimodally

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Advantages (2)

• Able to support superior error-handling compared
  with unimodal recognition interfaces
  
• User-centric reasons
• Users will select the input mode that they judge
  to be less error prone for a particular lexical
  context
  
• Users language is simplified when interacting
  multimodally
  
• Users have a strong tendency to switch modes
  after system errors
  
• System-centric reasons
• A well-designed multimodal architecture can
  support mutual disambiguation of input signals

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Advantages (3)

• Allow users to exercise selection and control
  over how they interact with the computer
  
• Hence can accommodate a broader range of users
• A visually impaired user may prefer speech input
  and TTS output
  
• A user with a hearing impairment strong accent
  or a cold may prefer pen input
  
• Multimodal interfaces are particularly suitable
  for supporting mobile tasks such as communication
  and personal navigation

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Types of Multimodal Architecture

• Can be subdivided into two main types
• Early Fusion
• Integrate signals at the feature level
• Based on Hidden Markov Models and Temporal Neural
  Networks
  
• The recognition process in one mode influences
  the course of recognition in the other
  
• Used for closely coupled and synchronized
  modalities (eg speech and lip movement)
  
• Systems tend not to apply or generalize as well
  if the modes differ substantially in the
  information content or time scale
  characteristics
• Require a large amount of training data to build
  the system.

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Types of Multimodal Architecture

• Late Fusion
• Integrate information at a semantic level
• Use individual recognizers trained using unimodal
  data
  
• Systems based on semantic fusion can be scaled up
  easier whether in the number of input modes or
  the size and type of the vocabulary sets
  
• Require an architecture that supports
  fine-grained time stamping of at least the
  beginning and end of each input signal
  
• Required to figure out if two signals are part of
  a multimodal construction or whether they should
  be interpreted as unimodal commands.

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Multimodal Architecture
Speech
Pen Glove Laser
Gesture Recognition
Speech Recognition
Gesture Understanding
NLP
Context Management
Multimodal Integration
Dialogue Manager
Graphics
VR
TTS
Application Invocation and Coordination
Response Planning
App1
App2
App3
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Applications

• OGI QuickSet System
• Enables a user to create and position entities on
  a map with speech pen-based gestures and direct
  manipulation.
  
• These entities are then used to initialize/run a
  simulation
  
• IBMs Human-Centric Word Processor
• Combines Natural Language understanding with
  pen-based pointing and selection gestures.
  
• Used to correct manipulate and format text after
  it has been entered
  
• Boeings Virtual Reality Aircraft Maintenance
  Training Prototype
  
• Used for accessing the maintainability of new
  aircraft designs and training mechanics in
  maintenance procedures using VR

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Applications

• Meditor Multimode Text Editor
• Combines keyboard Braille terminal a French
  text-to-speech synthesiser and a speech
  recognition system.
  
• Allows Blind people to perform simple Document
  editing tasks.
  
• MATCH
• Multimodal Access to City Help
• A Multimode Portable Device that accepts speech
  and pen gestures created by ATTT
  
• Allows mobile users to access restaurant and
  subway information for New York City

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Conclusion

• Multimodal systems are useful for a wide variety
  of applications
  
• They provide increased robustness ease of use
  and flexibility.
  
• They provide accessibility to computer to a wider
  and more diverse range of users
  
• However the area still needs a lot of research
  and a lot of challenges need to be overcome

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References

• Harald Aust et al. The Philips Automatic Train
  Timetable Information System Speech
  Communication 17 (1995) 249-262
  
• Gavin E. Churcher Eric S Atwell Clive Souter
  Dialogue Management Systems A Survey and
  Overview University of Leeds Research Report
  Series Report 97.06 Feb 1997
• Sharon J. Goldwater et al. Building a Robust
  Dialogue System with Limited Data ANLP-NAACL
  2000 Workshop Conversational Systems
  
• Staffan Larsson et al. GoDiS- An Accommodating
  Dialogue System ANLP-NAACL 2000 Workshop
  Conversational Systems
  
• Diane J. Litman and Shimei Pan Designing and
  Evaluating an Adaptive Spoken Dialogue System
  User Modelling and User-Adapted Interaction 12
  111-137 2002
• Michael F. McTear Spoken Dialogue Technology
  Enabling the conversational user interface ACM
  Computing Surveys Vol 34 No. 1 March 2002 pp.
  90-169
• Mikio Nakano et al. WIT A toolkit for building
  robust and real-time spoken dialogue systems 1st
  Sigdial Workshop at ACL2000
  
• Stephanie Seneff and Joseph Polifroni Dialogue
  Management in the Mercury Flight Reservation
  System ANLP-NAACL 2000 Workshop Conversational
  Systems May 2000 pp 11-16
• Satinder Singh et al. Optimizing Dialogue
  Management with Reinforcement Learning
  Experiments with the NJFun SystemJournal of
  Artificial Intelligence Research 16 (2002)
  105-133
• M. A. Walker et al. Evaluating spoken dialogue
  agents with PARADISE Two case studies Computer
  Speech and Language (1998) 12 317-347
  
• Wayne Ward and Bryan Pellom The CU Communicator
  System International Workshop on Automatic
  Speech Recognition and Understanding (1999)
  Section 5
• Sandra Williams Dialogue Management in
  Mixed-initiative Cooperative Spoken Language
  System11th Twente Workshop on Language
  Technology (TWLT11) Dialogue Management in
  Natural Language Systems Enschade Netherlands
  June 1996

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Questions