1 / 28

Grounding in Conversational Systems

Grounding in Conversational Systems. Dan Bohus January 2003 Dialogs on Dialogs Reading Group Carnegie Mellon University. Overview. Early grounding theories Discourse Contributions - Clark & Schaefer Conversational acts – Traum A Computational Framework (Horvitz, Paek) Principles Systems

kolton
Download Presentation

Grounding in Conversational Systems

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. Grounding in Conversational Systems Dan Bohus January 2003 Dialogs on Dialogs Reading Group Carnegie Mellon University

  2. Overview • Early grounding theories • Discourse Contributions - Clark & Schaefer • Conversational acts – Traum • A Computational Framework (Horvitz, Paek) • Principles • Systems • Grounding in RavenClaw

  3. Clark & Schaefer • In discourse, humans collaborate to establish/maintain mutual ground • Discourse is structured in contributions • Contribution : Presentation + Acceptance • Grounding criterion: “A and B mutually believe that the partners have understood what A said to a criterion sufficient for the current purposes”

  4. Clark & Schaefer (2) • Evidence of understanding: • Display • Demonstration • Acknowledgement • Initiating the next relevant contribution • Continued attention • Display/Demonstration order challenged…

  5. Clark & Schaefer (3) • Infinite recursion avoided by Strength of Evidence Principle • 4 possible states of non-understading • L did not notice S’s utterance • L notices it but does not hear it correctly • L hears it correctly but does not understand it • L understands it

  6. Traum • Conversational acts, extension of speech acts theory • Turn-taking • Grounding • Initiate, Continue, Cancel, ReqAck, Ack, ReqRepair, Repair • Core speech acts • Argumentational acts • Eliminates infinite recursion by: ack.s don’t need further ack.s

  7. Traum (2) • Later work, the following computational model is introduced: • Finally, Brennan (& Clark) • another computational formulation; • studies the different types of grounding behaviors in different media

  8. Criticisms • These models are by-and-large descriptive. • Can’t be used to determine what’s the next best thing to do to achieve the grounding criterion. • Moreover, they don’t describe quantitatively/make use of the uncertainty in contributions • Are insensitive to differences in channels, content, populations, etc… • Cannot be used for guidance • Decision Theory to the rescue ! ! !

  9. Decision Theory • Action under uncertainty • Given a set of states S = {s}, evidence e, and a set of actions A = {a}, if: • P(s|e) – is a probabilistic model of the state conditioned on the evidence • U(a,s) = the utility of taking action a when in state s. • Take action that maximizes the expected utility: • EU(a|e) = S U(a,s)*p(s|e)

  10. Conversation under Uncertainty • Conversation = action under uncertainty • Example: I want to fly to Pittsburgh … • States = {grounded, not_grounded} • Unaccessible, but describable by a probabilistic model • P(g | e) = P(Pittsburgh | e) … confidence annot. • Actions = {explicit_confirm, implicit_confirm, continue_dialog} • Utilities: • U(ec,g) < U(ic,g) < U(cd,g) • U(ec,ng) > U(ic,ng) > U(cd,ng)

  11. ec ic cd t2 t1 I want to fly to Pittsburgh (2) • States: • NotGrounded (ng) • Grounded (g) • Actions: • ExplicitConfirm (ec) • ImplicitConfirm (ic) • ContinueDialog (cd) • Utilities: • U(ec,g) < U(ic,g) < U(cd,g) • U(ec,ng) > U(ic,ng) > U(cd,ng) g ng

  12. Overview • Early grounding theories • Discourse Contributions - Clark & Schaefer • Conversational acts – Traum • A Computational Framework (Horvitz, Paek) • Principles • Systems • DeepListener • Bayesian Receptionist (Quartet architecture) • Presenter (Quartet architecture) • Grounding in RavenClaw

  13. DeepListener - Domain • Domain • Provides spoken command-and-control functionality for LookOut • Respond to offers of assistance (Yes/No) • Small domain, but illustrates the core ideas very well

  14. DeepListener - States • States: 5 possible “intentions” of the user • Acknowledgement • Negation • Reflection • Unrecognized Signal • No Signal • State model P(S|E) – temporal bayesian network. • E = User’s Actions, Content, ASR Results and Reliability + at time -1

  15. DeepListener - Actions • Actions: • Execute the service • Repeat • Note a hesitation and try again • Was that meant for me? • Try to get the user’s attention • Apologize for the interruption and forego the service • Troubleshoot the overall dialog

  16. DeepListener - Utilities • Utilities • Elicited through psychological experiments • Elicited through slidebars • Works when you have 2, 3 grounding actions, and a clear/small state-space design, but how about when the problem gets more complex ? • Example (paper)

  17. Bayesian Receptionist, Presenter • Bayesian Receptionist – performs the tasks of a receptionist at a MS front desk • “I’m here to see Rashid” • “Bathroom?” • “Beam me to 25 please” • … 32 goals • Presenter – command & control interface to PowerPoint presentations. • Both based on Quartet architecture

  18. Quartet • Uses DT and BN to ensure grounding at 4 different levels: • Signal • Channel • Intention • Conversation • The actual DM task is encapsulated in the same framework at the Intention level • Different domains = different intention levels

  19. Quartet – Signal & Channel • At each level infer a distribution over possible states. Key variables: • Signal level – signal identified (low/med/hi) • Channel level –user’s focus of attention • Maintenance module integrates Signal & Channel levels -> Maintenance Status: • Channel x Signal: NoChannel, NoSignal, ChannelButNoSignal, SignalButNoChannel, Signal

  20. Quartet – Intention Level • Domain is mostly goal inference • Hierarchical decomposition on levels, where lower levels refine the goals into more specific needs • Use BN to model p(goal | e) at leach level • Psychological studies to identify key variables and utilities • Visual cues • Linguistic variables; both syntactic and semantic

  21. Quartet – Intention Level • To move between levels, compare probability of goal to… • p-progress • (above: do it) • p-guess • (above: search confirmation) • (below: search more info via VOI) • p-backtrack • used on return nodes • Use Value-Of-Information analysis to infer what’s the variable that should be queried next.

  22. Comments on Intention level • What is the size of the learning problem? (How many BN needed?) How much data needed for training? • Not very clear : • how to deal with attribute/value, with rich ranges (e.g. which bus station ?) • how to deal with basically richer dialog mechanisms (beyond C&C applications) • focus shifts, mixed initiative • providing help

  23. Quartet – Conversation Level • See image. Use Intention and Maintenance Status to infer: • Grounding: diagnoses mutual understanding • Okay, ChannelFailure, IntentionFailure, ConversationFailure • Activity goal: measures if the user is engaged or not in an activity with the system • Compute expected utility for each action (utilities elicited through psychological studies)

  24. Bayesian Receptionist, Presenter • Runtime behavior (section 3) • Presenter • The Signal & Channel level allow a uniform treatment in the same framework of continuous listening • Experiments show that it’s better than random, but significantly less so than humans • But then again, the experiments were not very fair, being performed only at that level (i.e. no engaging in dialog allowed)

  25. My Research … • Deal with misunderstandings • Use probabilistic modeling and decision theory to make grounding decisions (but not task decisions) • I want a room tomorrow morning (0.73) • States: time correctly understood/not • Grounding Actions: no_action, expl_conf, impl_conf, reject • Utilities: try to learn them by relating the actions to an overall dialog/grounding metric

  26. RoomLine Login RoomLine Bye GetQuery ExecuteQuery DiscussResults Dialog Task Grounding Level Grounding Model Optimal action State/howwell are things going Strategies/Grounding Actions RavenClaw: Dialog Task / Grounding

  27. States and Actions • Actions Strategies.xls • States (have to keep it small!!!) • Single “state-space” model • What are the variables? Which are observable and which are stochastically modeled? • Multiple “state-space” models • First 5 strategies: state = amount of grounding on each concept • What should state be for the rest? What are the indicators? Which are fully observable and which are not? • How to combine decisions from different spaces

  28. Utilities • Learn them! How ? • Idea 1: POMDPs, maybe this small they are tractable • Idea 2: Regression to some overall dialog metric • What should that be? • (hmm) amount of non-null grounding actions taken • … • …

More Related