RavenClaw An improved dialog management architecture for task-oriented spoken dialog systems Presented by: Dan Bohus Work by: Dan Bohus, Alex Rudnicky, Andrew Hoskins Carnegie Mellon University, 2002

RavenClaw: a new DM architecture2 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 architecture3 A View from far, far away What did you just say ? Try opening that hatch SELECT * WHERE … Since that failed, I need you to push button B Can you repeat that, please ? Suspend… Resume … Conversational Skills Dialog Task Specification Backend Core  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

RavenClaw: a new DM architecture4 Outline  Goals  A view from far away  Main ideas  Dialog Task Specification / Execution  Conversational skills  In more detail  Dialog Task Specification / Execution  Conversational skills Conversational DTS Backend Core

RavenClaw: a new DM architecture5 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 DTS Communicator Welcome LoginTravelLocals Bye AskRegistered AskName GreetUserGetProfile Leg1 DepartLocationArriveLocation

RavenClaw: a new DM architecture6 Handling inputs Communicator Welcome LoginTravelLocals Bye AskRegistered AskName GreetUserGetProfile Leg1 DepartLocationArriveLocation  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 DTS

RavenClaw: a new DM architecture7 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. Conversational

RavenClaw: a new DM architecture8 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 Conversational

RavenClaw: a new DM architecture9 Outline  Goals  A view from far away  Main ideas  Dialog Task Specification / Execution  Conversational skills  In more detail  Dialog Task Specification / Execution  Conversational skills DTS

RavenClaw: a new DM architecture10 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 architecture11 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 architecture12 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 architecture13 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 architecture14 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 architecture15 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 architecture16 [Name] [Registered] [Hotel] [Bye] Input Pass 1. Construct an agenda of expectations  (Partially?) ordered list of concepts expected by the system [ArrivalCity][DepartureCity] Co Welcome LoginTravelLocals Bye Regist. Nam GreetProf. Leg1 DepArr Focused

RavenClaw: a new DM architecture17 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 [Name] [Registered] [Hotel] [Bye] [ArrivalCity] [DepartureCity] I’m flying to San Francisco and I need a hotel there.

RavenClaw: a new DM architecture18 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 architecture19 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 architecture20 Outline  Goals  A view from far away  Main ideas  Dialog Task Specification / Execution  Conversational skills  In more detail  Dialog Task Specification / Execution  Conversational skills Conversational Core

RavenClaw: a new DM architecture21 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 architecture22 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 architecture23 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 architecture24 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 architecture25 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 architecture26 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 architecture27 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 architecture28 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 architecture29 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 architecture30 Mechanisms  DepartureCity =  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 architecture31 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 architecture32 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 !