1 / 29

RavenClaw

RavenClaw. Yet another (please read “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 Carnegie Mellon University, 2002. New DM Architecture: Goals.

whitehead
Download Presentation

RavenClaw

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. RavenClaw Yet another (please read “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 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. Modeling the cost of misunderstanding …

  3. SELECT * WHERE … Backend Dialog Task Specification Since that failed, I need you to push button B What’s your name ? Conversational Skills Can you repeat that, please ? Suspend… Resume … What did you just say ? Core A View from far, far away • Let the developer focus only on the dialog task spec.: • Don’t worry about misunderstandings, repeats, focus shift, etc… merely describe (program) the task, assuming perfect knowledge of the world • Automatically generate the conversational mechanisms • Examples Modeling the cost of misunderstanding …

  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 Modeling the cost of misunderstanding …

  5. Dialog Task Spec & Execution • Agencies and Microagents (for input, request, execute …) • Handle 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 Communicator Welcome Login Travel Locals Bye AskRegistered GreetUser GetProfile Leg1 AskName DepartLocation ArriveLocation Modeling the cost of misunderstanding …

  6. 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 Communicator Welcome Login Travel Locals Bye AskRegistered GreetUser GetProfile Leg1 AskName DepartLocation ArriveLocation Modeling the cost of misunderstanding …

  7. 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. Modeling the cost of misunderstanding …

  8. Conversational Skills / Mechanisms • 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 • The core takes care of these by dynamically inserting in the task tree agencies which handle these mechanisms. Modeling the cost of misunderstanding …

  9. 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 Modeling the cost of misunderstanding …

  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 … ? Modeling the cost of misunderstanding …

  11. Dialog Task Specification • A possible more formalized approach • Tree of agents with: • Preconditions • Success Criteria • Focus Criteria (triggers) • Expressed mostly in terms of concepts • Data, Type (basic, struct, array) • Confidence, Availability, Ambiguousness, Groundedness, System/User, TurnAcquired, TurnConveyed, etc… Modeling the cost of misunderstanding …

  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. Modeling the cost of misunderstanding …

  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. Modeling the cost of misunderstanding …

  14. Other Ideas for DTS • 4 Microagents: Inform, Request, Expect, Execute • Provide a library of “common task” and “common discourse” agencies • Frame agency • List browse agency • Choose agency • Disambiguate agency, Ground Agency, … • Etc Modeling the cost of misunderstanding …

  15. DTS execution • Agency.Execute() decides what is executed next • 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 Modeling the cost of misunderstanding …

  16. Input Pass 1. Construct an agenda of expectations • (Partially?) ordered list of concepts expected by the system 2. Bind values/confidences to concepts • The SI <> MI spectrum can be expressed in terms of the way the agenda is constructed and binding policies, independent of task 3. Process non-understandings (iff) - try and detect source and inform user: • Channel (SNR, clipping) • Decoding (confidence score, prosody) • Parsing ([garble]) • Dialog level (POK, but no expectation) Modeling the cost of misunderstanding …

  17. 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) Modeling the cost of misunderstanding …

  18. 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 Modeling the cost of misunderstanding …

  19. Task-Independent, Conversational Mechanisms • Should be transparently handled by the core; little or no effort from the developer • However, the developer should be able to write his own customized mechanisms if needed • Handled by inserting extra “discourse” agents on the fly in the dialog task specification Modeling the cost of misunderstanding …

  20. Conversational Skills • Universal dialog mechanisms: • Repeat, Suspend… Resume, Help, Start over, Summarize, Undo, Querying the system’s belief • The grounding / misunderstanding problems • 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 ? Modeling the cost of misunderstanding …

  21. 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 Modeling the cost of misunderstanding …

  22. 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 Modeling the cost of misunderstanding …

  23. Suspend … Resume • DTT adorned with a SuspendResume agency. • Forces a context reestablishment on the current main topic upon resume. • Context reestablishment also happens when focusing back after a sub-dialog • Can maybe construct a model for that (given size of sub-dialog, time issues, etc) Modeling the cost of misunderstanding …

  24. Start over, Summarize, Querying • Start over: • DTT adorned with a Start-Over agency • Summarize: • DTT adorned with a Summarize agency; prompt generated automatically, problem shifted to NLG: can we do something corpus-based … work on automated summarization ? • Querying the system’s beliefs: • Still thinking… problem with the grammars… can meaningful Phoenix grammars for “what is [slot]” be automatically generated ? Modeling the cost of misunderstanding …

  25. Timing & barge-in control • Knowledge of barge-in location • Information on what got conveyed is fed back to the DM, through the concepts to the task level • 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 TI manner ? Modeling the cost of misunderstanding …

  26. Confirmation, Clarif., Disamb., Misunderstandings, Grounding… • Largely unsolved in my head: this is next ! • 2 components: • Confidence scores 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 does one decide ? Modeling the cost of misunderstanding …

  27. Confidence scores • Obtaining conf. Scores : from annotator • Updating them, from different sources: • (Un)Attacked implicit/explicit confirms • Correction detection • Elapsed time ? • Domain knowledge • Priors ? • But how do you integrate all these in a principled way ? Modeling the cost of misunderstanding …

  28. 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 ? Modeling the cost of misunderstanding …

  29. 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 Modeling the cost of misunderstanding …

More Related