RavenClaw

1. 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

2. New DM Architecture: Goals • Able to handle complex, goal-directed dialogs • Go beyond (information access systems and) the slot-filling paradigm • Easy to develop and maintain systems • Developer focuses only on dialog task • Automatically ensure a minimum set of task-independent, 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. RavenClaw: a new DM architecture

3. Backend Dialog Task Specification Conversational Skills Core A View from far, far away • Let the developer focus only on the dialog task spec.: • 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 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 ? RavenClaw: a new DM architecture

4. Backend DTS Conversational Core Outline • Goals • A view from far away • Main ideas • Dialog Task Specification / Execution • Conversational skills • In more detail • Dialog Task Specification / Execution • Conversational skills RavenClaw: a new DM architecture

5. Communicator Welcome Login Travel Locals Bye AskRegistered GreetUser GetProfile Leg1 AskName DTS DepartLocation ArriveLocation Dialog Task Spec & Execution • Dialog Task implemented by a hierarchy of agents • Handle and Operate based on concepts • Execution with interleaved Input Passes. • Execute the agents by top-down “planning” • Do input passes when information is required • REMEMBER: This is just the dialog task RavenClaw: a new DM architecture

6. Communicator Welcome Login Travel Locals Bye AskRegistered GreetUser GetProfile Leg1 AskName DTS DepartLocation ArriveLocation Handling inputs • Input Pass • 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 RavenClaw: a new DM architecture

7. Conversational Conversational Skills /Mechanisms • A lot of problems in SDS generated by lack of conversational skills. “It’s all in the little details!” • 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. RavenClaw: a new DM architecture

8. Conversational Conversational Skills / Mechanisms • The core takes care of these by dynamically inserting 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: • Repeat, Suspend… Resume, Help, Start over, Summarize, Undo, Querying the system’s belief RavenClaw: a new DM architecture

9. DTS Outline • Goals • A view from far away • Main ideas • Dialog Task Specification / Execution • Conversational skills • In more detail • Dialog Task Specification / Execution • Conversational skills RavenClaw: a new DM architecture

10. Dialog Task Specification • Goal: able to handle complex domains, beyond information access, frame-based, slot-filling systems i.e. : • Symphony, Intelligent checklists, Navigation, Route planning • We need a powerful enough formalism to describe all these tasks: • C++ code ? • Declarative would be nice … but is it powerful enough ? • Templatized C++ code … ? RavenClaw: a new DM architecture

11. Dialog Task Specification • Tree of predefined agents types: • Inform, Request, Expect, Execute • Each agent has: • A set of concepts • Preconditions • Success Criteria • Effects • Focus Criteria (triggers) • Concepts • Data, Type (basic, struct, array) • Confidence/Value, Availability, Ambiguousness, Groundedness, System/User, TurnAcquired, TurnConveyed, etc… RavenClaw: a new DM architecture

12. 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. RavenClaw: a new DM architecture

13. Can a formalism cut it ? • People have repeatedly tried formalizing dialog … and failed  • 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. RavenClaw: a new DM architecture

14. DTS execution • Agency.Execute() decides which subagent is executed next, based on preconditions • Various simple policies can be implemented • Left-to-right (open/closed), choice, etc • But free to do more sophisticated things (MDPs, etc) ~ learning at the task level RavenClaw: a new DM architecture

15. Libraries of DTS agencies ? • Provide a library of “common task” and “common discourse” agencies • Frame agency • List browse agency • Choose agency • Disambiguate agency, Ground Agency, … • Etc RavenClaw: a new DM architecture

16. Co Welcome Login Travel Locals Bye Regist. Greet Prof. Leg1 Nam [DepartureCity] [ArrivalCity] Dep Arr [Name][Registered][Hotel][Bye] Input Pass 1. Construct an agenda of expectations • (Partially?) ordered list of concepts expected by the system Focused RavenClaw: a new DM architecture

17. [DepartureCity] [ArrivalCity] [Name][Registered][Hotel][Bye] Input Pass (continued) 2. Bind values/confidences to concepts • The System <> Mixed Initiative spectrum can be expressed in terms of the way the agenda is constructed and binding policies, independent of task I’m flying to San Francisco andI need a hotel there. RavenClaw: a new DM architecture

18. Input pass (continued) 3. Process non-understandings (iff) - try and detect source and inform user: • Channel (SNR, clipping) • Decoding (confidence score, prosody) • Parsing (parsing scores) • Dialog level (parse ok, but no expectation match) RavenClaw: a new DM architecture

19. Input Pass 4. Focus shifts • 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) RavenClaw: a new DM architecture

20. Conversational Core Outline • Goals • A view from far away • Main ideas • Dialog Task Specification / Execution • Conversational skills • In more detail • Dialog Task Specification / Execution • Conversational skills RavenClaw: a new DM architecture

21. Task-Independent, Conversational Mechanisms • Should be transparently handled by the core • 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 RavenClaw: a new DM architecture

22. Conversational Skills: A List • The grounding / misunderstandings problems • Universal dialog mechanisms: • 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 ? RavenClaw: a new DM architecture

23. UDM: Repeat • Repeat (simple) • 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… ? • Repeat (with referents) • only 3%, they are mostly [summarize] • User-defined custom repeat agency RavenClaw: a new DM architecture

24. UDM: Help • DTT adorned at start-up with a help agency • Can capture and issue: • Local help (obtained from focused agent) • ExplainMore help (obtained from focused) • 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 RavenClaw: a new DM architecture

25. UDM: Suspend … Resume • DTT adorned with a SuspendResume agency. • Context reestablishment • Automatically when focusing back after a sub-dialog • Construct a model for that (given size of sub-dialog, time issues, etc) • Prompts problem shifted to the NLG RavenClaw: a new DM architecture

26. UDM: Start over, Summarize • Start over: • DTT adorned with a Start-Over agency • Summarize: • DTT adorned with a Summarize agency • prompt generated automatically • problem shifted to NLG … RavenClaw: a new DM architecture

27. Timing & barge-in control • Knowledge of barge-in location • Information on what got conveyed is fed back to the DM • 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 ? RavenClaw: a new DM architecture

28. Confirmation, Clarif., Disamb., Misunderstandings, Grounding… • Largely unsolved: this is next ! • 2 components: • Confidence scores/computation on concepts • Obtaining them • Updating them • Taking the “right” decision based on those scores: • 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 ? RavenClaw: a new DM architecture

29. Confidence scores • Obtaining conf. Scores: from annotator • Updating them, from different sources: • (Un)Attacked implicit/explicit confirms • Correction detector • Elapsed time ? • Domain knowledge • Priors ? • But how do you integrate all these in a principled way ? RavenClaw: a new DM architecture

30. Mechanisms • DepartureCity = <Seattle,0.71><SF,0.29> • Implicit / Explicit confirmations • When do you leave from Seattle ? • So you’re leaving from Seattle… When ? • Clarifications • Did you say you were leaving from Seattle ? • Disambiguation • I’m sorry was that Seattle or San Francisco? • How do you decide which ? • Learning ? RavenClaw: a new DM architecture

31. Software Engineering • Provide a robust basis for future research. • Modularity • Separability between task and discourse • Separability of concepts and confidence computations • Portability • Mutiple servers • Aggressive, structured, timed logging RavenClaw: a new DM architecture

32. Conclusion • New DM framework • separation of dialog task from conversational mechanisms • 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 ! RavenClaw: a new DM architecture