dialog manager, ravenclaw

2. • Dialog Management System
• Dialog Manager
• Dialog Management Technologies
• Main features in Ravenclaw
• 2-tier architecture
• Dialog Task Specification
• Dialog Agents
• Agent Behavior
• Concept
• DTS in Ravenclaw
• Hierarchical Plan-based representation
• Core Algorithm
• Execute Phase
• Stack & Tree
• Input phase
• Expectation agenda
• Mixed initiative
• Control the amount of initiative
• Context-based semantic disambigution
• Error handling

3. Dialog Management System

4. • determines the next system action with
– Current semantic input
– Current dialog state
– Information about the task
• more-complex model
– Timing, turn-taking
– Barge-ins, backchannels,
multi-participant conversation
– Error handling
– Various appropriate requests
Dialog Manager

5. • Finite-state dialog manager
• Form-filling (frame-based) dialog manager
• Plan-based dialog manager (RavenClaw)
– Model the goals of the conversation
– Guide the dialog along the path
towards these goals
Dialog Management Technologies

6. • Task-independence
– Clear separation between
– the domain-specific aspects of the dialog control l
ogic
– And domain-independent
– Reusable dialog control mechanism
• Flexibility
• Scalability
• Transparency
• Modularity
• …
Main features in RavenClaw

7. 2-tier architecture

8. • DTS(Dialog Task Specification)
– Covers domain specific aspects
– Describes hierarchical plan
– Consist of a tree of dialog agents
Dialog Task Specification

9. • Types
• Inform
–Generate output(e.g. greeting)
• Request
–Request information
–Collect the user’s response
• Expect
–Expect information from user
• Execute
–Database access
–Api calls
–Etc
• Dialog Agencies
• Planning the execution of their sub-agents
Dialog Agents

10. • Execute routine
– Basic operations of each agent(4 types)
• Additional configuration for agent/agency
– Precondition
– Trigger
– Success/fail criteria
Agent Behavior

11. Concept

12. • Type
– Boolean, string, integer, float, structure, array
• Value/Confidence
– Ex. City_name = {Boston/0.35, Austin/0.27}
– Due to speech recognition error
– Not used in our Chatbot
• Information(value) maintained in scenario
– History of previous values
– Grounding state
– …
Concept

13. DTS in RavenClaw

14. Described in DialogTask.cpp with MACRO
DTS in RavenClaw

15. • No fixed order
– Depends on user input,
encoded domain constraint,
task logic and various execution policies
• Hierarchical plan-based representation
– Most goal-oriented dialog tasks have a naturally
hierarchical structure
– Sub-components are independent
Ease in design, maintenance, scalability and reusability
– Can be extended at run time
Allow for the dynamic construction
Hierarchical Plan-based representation

16. • 2 data structures
– Dialog stack
– Expectation agenda
• 2 phase
– Execution phase
– Input phase
Core Algorithm

17. Execute Phase

18. • Execute agent on top of the stack
– Inform agent : output system prompt
– Request agent – output system prompt  input phase
– Expect agent – do nothing(just expect)
– Execute agent – call APIs
– Agency – push one of subagents on the dialog stack
• Eliminate completed agents from stack
– If the completion condition is meet.
• Push focus claiming agents on stack
– Inspects the focus claims(trigger) conditions.
Execute Phase

19. • Dialog Stack
– Temporal and hierarchical structure of the discourse
• Tree(DTS)
– Hierarchical goal structure of the task
• Isomorphism between stack and tree
– Sometimes broken when focus shifts
If trigger condition meets, push the agent on the
stack Isomorphism will be maintained
after focus claimed agents finish
Stack & Tree

20. • Only in Request agent
– Assemble the expectation agenda
• Waits for an user input
– Thread blocked until user input
• Update concept (concept binding)
– Top-down traversal of the expectation agenda
Input phase

21. Input phase

22. • Data-structure that describes
what the system expects to hear from the
user in the current turn
– Advantages
Allow over-answer
Mixed-initiative interaction
Automatically performs context-based semantic
disambiguation
Dynamic state-specific language modeling
Expectation agenda

23. Mixed initiative

24. • Focus claim if agenda is open.
• By default, open only when under the sam
e topic
– If main topic is set, child agent(agency) can be
focus-shifted.
앞의 예제에서 Hotels가 main topic으로 설정되어
있었다면, focus shift가 되지 않음.
• Expectation scope operators
– ! – open if expectation == focus
– * - always open
– @ - open if agents which are listed
after @ == focus
Control the amount of initiative

25. Context-based semantic disambiguation

26. • State/context-specific language model
• Level-based organization of
expectation agenda provides additional
information
• But NOT yet implemented
in RavenClaw engine
Dynamic state-specific language modeling

27. • Speech recognition and
language understanding
– Still far from perfect
– Non-understanding & misunderstanding
• Domain-independent error handling
– Various advantages
Error handling

28. • No speech recognition in chatbot
– No confidence level
• No explicit error decision process
In Chatbot

29. • Data flow
• Message  NLU  DM  NLG  Message
NLU  DM  NLG

30. DTS – json format

31. • Original RavenClaw is c-based.
– DTS  JSONize
– RavenClaw engine : c  python
• New classes
• New properties
Customize