Grounding in Conversational Systems

Dan BohusJanuary 2003Dialogs on Dialogs Reading GroupCarnegie Mellon University

Overview Early grounding theories

Discourse Contributions - Clark & SchaeferConversational acts – Traum

A Computational Framework (Horvitz, Paek)PrinciplesSystems

Grounding in RavenClaw

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”

Clark & Schaefer (2) Evidence of understanding:

DisplayDemonstrationAcknowledgement Initiating the next relevant contributionContinued attention

Display/Demonstration order challenged…

Clark & Schaefer (3) Infinite recursion avoided by Strength of

Evidence Principle 4 possible states of non-understading

L did not notice S’s utteranceL notices it but does not hear it correctlyL hears it correctly but does not understand itL understands it

Traum Conversational acts, extension of speech

acts theoryTurn-takingGrounding

Initiate, Continue, Cancel, ReqAck, Ack, ReqRepair, Repair

Core speech actsArgumentational acts

Eliminates infinite recursion by: ack.s don’t need further ack.s

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

)())()(()()(

CGGGCU

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 ! ! !

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 evidenceU(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)

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)

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)

ng g

ec

ic

cd

t1 t2

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

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

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

DeepListener - Actions Actions:

Execute the serviceRepeatNote a hesitation and try againWas that meant for me?Try to get the user’s attentionApologize for the interruption and forego the

serviceTroubleshoot the overall dialog

DeepListener - Utilities Utilities

Elicited through psychological experimentsElicited through slidebarsWorks 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)

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

Quartet Uses DT and BN to ensure grounding at 4

different levels:SignalChannel IntentionConversation

The actual DM task is encapsulated in the same framework at the Intention levelDifferent domains = different intention levels

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

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 levelPsychological studies to identify key variables

and utilities Visual cues Linguistic variables; both syntactic and semantic

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.

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

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)

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)

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/notGrounding Actions: no_action, expl_conf,

impl_conf, rejectUtilities: try to learn them by relating the

actions to an overall dialog/grounding metric

RavenClaw: Dialog Task / Grounding RoomLine

Login RoomLine

GetQuery

Bye

ExecuteQuery DiscussResults

Dialog Task

Grounding Model

Grounding Level

Strategies/Grounding Actions

Optimal actionState/howwell are thingsgoing

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

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 …

…