RavenClaw
An improved dialog management architecture for task-oriented
spoken dialog systems
Presented by: Dan Bohus (dbohus@cs.cmu.edu)
Work by: Dan Bohus, Alex Rudnicky, Andrew Hoskins
Carnegie Mellon University, 2002
New DM Architecture: Goals
 Able to handle complex, goal-directed dialogs
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Easy to develop and maintain systems
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Go beyond (information access systems and)
the slot-filling paradigm
Developer focuses only on dialog task
Automatically ensure a minimum set of taskindependent, conversational skills
Open to learning (hopefully both at task and
discourse levels)
Open to dynamic SDS generation
More careful, more structured code, logs, etc:
provide a robust basis for future research.
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A View from far, far away
SELECT * WHERE …
Try opening that hatch
Since that failed, I need you to push
button B
Can you repeat that, please ?
Suspend… Resume …
What did you just say ?
Backend
Dialog Task Specification
Conversational Skills
Core
 Let the developer focus only on the dialog task spec.:
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Don’t worry about misunderstandings, the accuracy of
concepts, repeats, focus shifts, barge-ins, etc… merely
describe (program) the task, assuming perfect knowledge of
the world
Automatically generate the conversational mechanisms
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Backend
Outline
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DTS
Conversational
Goals
A view from far away
Main ideas
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Core
Dialog Task Specification / Execution
Conversational skills
 In more detail
 Dialog Task Specification / Execution
 Conversational skills
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Dialog Task Spec & Execution
Communicator
Welcome
Login
Travel
Locals
Bye
DTS
AskRegistered
AskName
GreetUser
GetProfile
DepartLocation
Leg1
ArriveLocation
 Dialog Task implemented by a hierarchy of agents
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Handle and Operate based on concepts
 Execution with interleaved Input Passes.
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Execute the agents by top-down “planning”
Do input passes when information is required
 REMEMBER: This is just the dialog task
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Handling inputs
Communicator
Welcome
Login
Travel
Locals
Bye
DTS
AskRegistered
AskName
GreetUser
GetProfile
DepartLocation
Leg1
ArriveLocation
 Input Pass
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Assemble an agenda of expectations (open concepts)
Bind values from the input to the concepts
Process non-understanding (if), analyze need for focus shifts
Continue execution
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Conversational Skills /
Mechanisms
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A lot of problems in SDS generated by lack of
conversational skills. “It’s all in the little details!”
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Conversational
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Dealing with misunderstandings
Generic channel/dialog mechanisms : repeats, focus
shift, context establishment, help, start over, etc, etc.
Timing
Even when these mechanisms are in, they lack
uniformity & consistency.
Development and maintenance are time
consuming.
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Conversational Skills /
Mechanisms
 The core takes care of these by dynamically inserting
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Conversational
appropriate agencies in the task tree
A list of (more or less) task independent mechanisms:
 Implicit/Explicit Confirmations, Clarifications,
Disambiguation = the whole Misunderstandings problem
 Context reestablishment
 Timeout and Barge-in control
 Back-channel absorption
 Generic dialog mechanisms:

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Repeat, Suspend… Resume, Help, Start over, Summarize, Undo,
Querying the system’s belief
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Outline
DTS
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Goals
A view from far away
 Main ideas
 Dialog Task Specification / Execution
 Conversational skills
 In more detail
 Dialog Task Specification / Execution
 Conversational skills
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Dialog Task Specification
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Goal: able to handle complex domains, beyond
information access, frame-based, slot-filling
systems i.e. :
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Symphony, Intelligent checklists, Navigation, Route
planning
We need a powerful enough formalism to
describe all these tasks:
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C++ code ?
Declarative would be nice … but is it powerful enough ?
Templatized C++ code … ?
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Dialog Task Specification
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Tree of predefined agents types:
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Each agent has:
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Inform, Request, Expect, Execute
A set of concepts
Preconditions
Success Criteria
Effects
Focus Criteria (triggers)
Concepts
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Data, Type (basic, struct, array)
Confidence/Value, Availability, Ambiguousness,
Groundedness, System/User, TurnAcquired,
TurnConveyed, etc…
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An example DTS
UserLogin: AGENCY
concepts: registered(BOOL), name(STRING), id(STRING),
profile(PROFILE), profile_found(BOOL)
achieves_when: profile || InformProfileNotFound
AskRegistered: REQUEST(registered)
grammar: {[yes]->true,[no]->false,[guest]->false}
AskName: REQUEST(name)
precond: registered==no
grammar: [user_name]
max_attemps: 2
InformGreetUser: INFORM
precond: name
AskID: REQUEST(id)
precond: registered==yes
mapping: [user_id]
DoProfileRetrieval: EXECUTE
precond: name || id
call: ABEProfile.Call >name, >id, <profile, <profile_found
InformProfileNotFound: INFORM
precond: !profile_found
Given that the baseline is 259 lines of C++ code, this is pretty good.
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Can a formalism cut it ?
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People have repeatedly tried formalizing
dialog … and failed 
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We’re focusing only on the task (like in
robotics/execution)
Actually, these agents are all C++ classes, so
we can backoff to code; the hope is that most
of the behaviors can be easily expressed as
above.
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DTS execution
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Agency.Execute() decides which subagent
is executed next, based on preconditions
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Various simple policies can be implemented
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Left-to-right (open/closed), choice, etc
But free to do more sophisticated things
(MDPs, etc) ~ learning at the task level
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Libraries of DTS agencies ?
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Provide a library of “common task” and
“common discourse” agencies
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Frame agency
List browse agency
Choose agency
Disambiguate agency, Ground Agency, …
Etc
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Input Pass
1. Construct an agenda of expectations
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(Partially?) ordered list of concepts expected
by the system
[DepartureCity]
Co
[ArrivalCity]
Welcome
Regist.
Nam
Login
Greet
Travel
Prof.
Dep
Locals
Bye
[Name]
[Registered]
[Hotel]
[Bye]
Leg1
Arr
Focused
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Input Pass (continued)
2. Bind values/confidences to concepts
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The System <> Mixed Initiative spectrum can be
expressed in terms of the way the agenda is constructed
and binding policies, independent of task
[DepartureCity]
I’m flying to San Francisco and
I need a hotel there.
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[ArrivalCity]
[Name]
[Registered]
[Hotel]
[Bye]
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Input pass (continued)
3. Process non-understandings (iff) - try and
detect source and inform user:
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Channel (SNR, clipping)
Decoding (confidence score, prosody)
Parsing (parsing scores)
Dialog level (parse ok, but no expectation
match)
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Input Pass
4. Focus shifts
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Focus shifts seem to be task dependent.
Decision to shift focus is taken by the task
(DTS)
But they also have a TI-side (sub-dialog size,
context reestablishment). Context
reestablishment is handled automatically, in
the Core (see later)
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Outline
Conversational

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Goals
A view from far away
 Main ideas
Core
 Dialog Task Specification / Execution
 Conversational skills
 In more detail
 Dialog Task Specification / Execution
 Conversational skills
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Task-Independent, Conversational
Mechanisms
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Should be transparently handled by the
core
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However, the developer should be able to write
his own customized mechanisms if needed
Most cases handled by inserting extra
“discourse” agents on the fly in the dialog
task tree
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Conversational Skills: A List
 The grounding / misunderstandings problems
 Universal dialog mechanisms:
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Repeat, Suspend… Resume, Help, Start over, Summarize, Undo,
Querying the system’s belief
Timing and Barge-in control
Focus Shifts, Context Establishment
Back-channel absorption
 Q: To which extent can we abstract these away
from the Dialog Task ?
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UDM: Repeat
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Repeat (simple)
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Repeat (with referents)
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The DTT is adorned with a “Repeat” Agency
automatically at start-up
Which calls upon the OutputManager
Not all outputs are “repeatable” (i.e. implicit
confirms, gui, )… which ones exactly… ?
only 3%, they are mostly [summarize]
User-defined custom repeat agency
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UDM: Help
 DTT adorned at start-up with a help agency
 Can capture and issue:
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Local help (obtained from focused agent)
ExplainMore help (obtained from focused)
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What can I say ?
Contextual help (obtained from main topic)
Generic help (give_me_tips)
Obtains Help prompts from the focused agent and
the main topic (defaults provided)
Default help agency can be overwritten by user
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UDM: Suspend … Resume
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DTT adorned with a SuspendResume
agency.
Context reestablishment
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Automatically when focusing back after a subdialog
Construct a model for that (given size of subdialog, time issues, etc)
Prompts problem shifted to the NLG
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UDM: Start over, Summarize
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Start over:
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DTT adorned with a Start-Over agency
Summarize:
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DTT adorned with a Summarize agency
prompt generated automatically
problem shifted to NLG …
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Timing & barge-in control
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Knowledge of barge-in location
Information on what got conveyed is fed
back to the DM
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Special agencies can take special action
based on that (I.e. List Browsing)
Can we determine what are non-barge-in-able
utterances in a task-independent manner ?
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Confirmation, Clarif., Disamb.,
Misunderstandings, Grounding…
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Largely unsolved: this is next !
2 components:
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Confidence scores/computation on concepts
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Obtaining them
Updating them
Taking the “right” decision based on those
scores:
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Insert appropriate agencies on the fly in the dialog
task tree: opportunity for learning
What’s the set of decisions / agencies ?
How do you decide ?
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Confidence scores
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Obtaining conf. Scores: from annotator
Updating them, from different sources:
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(Un)Attacked implicit/explicit confirms
Correction detector
Elapsed time ?
Domain knowledge
Priors ?
But how do you integrate all these in a
principled way ?
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Mechanisms
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DepartureCity = <Seattle,0.71><SF,0.29>
Implicit / Explicit confirmations
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Clarifications
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Did you say you were leaving from Seattle ?
Disambiguation
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When do you leave from Seattle ?
So you’re leaving from Seattle… When ?
I’m sorry was that Seattle or San Francisco?
How do you decide which ?
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Learning ?
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Software Engineering
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Provide a robust basis for future research.
Modularity
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Separability between task and discourse
Separability of concepts and confidence
computations
Portability
Mutiple servers
Aggressive, structured, timed logging
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Conclusion
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New DM framework
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separation of dialog task from conversational
mechanisms
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developer can focus only on dialog task
conversational mechanisms generated automatically
easier development/maintenance
robust platform for future research
Most of the implementation completed
Symphony/LARRI reimplemented
Next: back to misunderstandings !
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