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Generating Feedback and Sequencing Moves in a Dialogue System. AAAI Spring Symposium 2003 Staffan Larsson Göteborg University, Sweden. Overview. Interactive Communication Management (ICM) ”Verification” in dialogue systems Classifying and formalising ICM ICM for a dialogue system Examples

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generating feedback and sequencing moves in a dialogue system

Generating Feedback and Sequencing Moves in a Dialogue System

AAAI Spring Symposium 2003

Staffan Larsson

Göteborg University, Sweden

  • Interactive Communication Management (ICM)
  • ”Verification” in dialogue systems
  • Classifying and formalising ICM
  • ICM for a dialogue system
  • Examples
  • Conclusions & Future work
icm allwood
ICM (Allwood)
  • Interactive Communication Management
    • As opposed to Own Communication Management (OCM): self-corrections, hesitations, etc.
  • Feedback moves
    • (short) utterances which signal grounding status of previous utterance (”mm”, ”right”, ”ok”, ”pardon?”, ”huh?” etc.)
  • Sequencing moves
    • utterances which signal dialogue structure (”so”, ”now”, ”right”, ”anyway” etc.)
    • Dialogue structure part of / modeled by common ground
  • Turntaking moves
grounding and icm in current commercial systems
Grounding and ICM in current commercial systems
  • Limited to ”verification”
  • Examples (San Segundo et. al. 2001)
    • I understood you want to depart from Madrid. Is that correct? [”explicit v.”]
    • You leave from Madrid. Where are you arriving at? [”implicit v.”]
  • Involves repetition or reformulation
  • Appears in H-H dialogue, but not very common
from verification to icm in dialogue systems
From verification to ICM in dialogue systems
  • ”Verification” is just one type of ICM behaviour
    • Perhaps the one most cruicial in dialogue systems given poor speech recognition
  • Could a wider range of the ICM behaviour occurring in H-H dialogue be useful in dialogue systems?
  • We want a typology of ICM moves for H-H dialogue
    • Feedback and sequencing moves
  • We want to formalise it and use it in a system
    • Still we will implement only a subset, but more than verification
classifying feedback
Classifying feedback
  • Level of action
  • Polarity
  • Eliciting / noneliciting
  • Form (syntactic realisation)
  • Content type: object- or metalevel
feedback levels
Feedback levels
  • Action levels in dialogue (Allwood, Clark, Ginzburg)
    • Contact: whether a channel of communication is established
    • Perception: whether DPs are perciveving each other’s utterances
    • Understanding: Whether DPs are understanding each other’s utterances
      • Non-contextual (”semantic”) meaning
      • Contextual (”pragmatic”) meaning
    • Acceptance: Whether DPs are accepting each other’s utterances
  • The function of feedback is to signal the status of utterance processing on all levels
feedback polarity
Feedback polarity
  • Polarity (Allwood 1992)
    • Positive: indicates contact, perception, understanding, acceptance
    • Negative: indicates lack of contact, perception, understanding, acceptance
    • We add a ”neutral” or ”checking” polarity – there is one or more hypotheses, but the DP lacks confidence in them
  • Examples
    • ”I don’t understand”: negative
    • ”Do you mean that the destination is Paris?”: checking
    • ”To Paris.”: positive
    • ”Pardon”: negative
formalising icm dialogue moves
Formalising ICM dialogue moves
  • Action levels
    • con: contact
    • per: perception
    • sem: semantic understanding (no context)
    • und: pragmatic understanding (relevance in context)
    • acc: acceptance
  • Polarity
    • pos
    • neg
    • chk (”int” in paper)
feedback move notation
Feedback move notation
  • icm:Level*Polarity{:Args}
    • icm:per*pos:String – ”I heard you say ’londres’”
    • icm:und*neg – ”Sorry, I don’t understand”
    • icm:und*chk:AltQ – ”Do you mean x or y?”
    • icm:und*pos:P – ”To Paris.”
    • icm:acc*neg:Q – ”Sorry, I can’t answer Q”
    • icm:acc*pos – ”Okay”
system feedback for user utterances in godis
System feedback for user utterances in GoDIS
  • contact
    • negative (”I didn’t hear anything from you.”, ”Hello?”) [icm:con*neg]
  • perception
    • negative: fb-phrase (”Pardon?”, ”I didn’t hear what you said”) [icm:per*neg]
    • positive: repetition (”I heard ’to paris’”) [icm:per*pos:String]
  • semantic understanding:
    • negative: fb-phrase (”I don’t understand”) [icm:sem*neg]
    • positive: reformulation (”Paris.”) [icm:sem*pos:Content]
    • checking: reformulation (”Paris, is that correct?”, ”Paris?”) [icm:sem*chk:Content]
system feedback cont d
System feedback, cont’d
  • pragmatic understanding
    • negative: fb-phrase (”I don’t quite understand”) [icm:und*neg]
    • positive: reformulation (”To Paris.”) [icm:und*pos:Content]
    • checking: reformulation (”To Paris, is that correct?”, ”To Paris?”) [icm:und*chk:Content]
  • acceptance/integration
    • negative:fb-phrase with reformulation (”Sorry, I cannot answer Q”, ”Sorry, Paris is not a valid destination city.”) [icm:acc*neg:Content]
    • positive: fb-word (”okay.”) [icm:acc*pos]
feedback selection
Feedback selection
  • Feedback polarity selected depending on (for each level respectively):
    • Contact: Whether any speech is observed
    • Perception: Speech recognition score
    • Semantics: Whether system can find a non-contextual interpretation
    • Pragmatic understanding: Whether system can find a relevant contextual interpretation
    • Acceptance: Whether system can accept what’s been said
feedback on multiple levels
Feedback on multiple levels

Negative perception feeback

S> Lets see. What can I do for you?

U> (mumble mumble)

S> Sorry, I didnt hear what you said. What can I do for you?

U> siridus

S> I heard you say siridus . Sorry, I dont understand. What can I

do for you?

U> channel one

S> channel one. I dont quite understand. Do you want to add a

program or change channel?

Positive perception

Negative understanding

Positive understanding (word meaning)

Negative understanding (relevance)

(Task clarification)





(negative acceptance feedback)

U> turn on the t v

S> turn on the TV… Sorry, I cannot control the TV set. Do you want to add a program or change channel?”

U> pardon

S> Sorry, I cannot control the TV set. Do you want to add a program or change channel?

U> change channel

S> Okay.

User negative perception

System repetition



sequencing icm
Sequencing ICM
  • Discourse particles
    • ”so” may be used to reraise a previously discussed question
    • ”right”, ”anyway”, ”let’s see…”
  • Verbose sequencing
    • ”Let’s talk about …”, ”Let’s return to the issue of …”
  • How do we classify these?
    • We will correlate them with updates to the common ground
sequencing moves generated by godis
Sequencing moves generated by GoDiS
  • Formal notation schema:
    • icm:Type{:Content}
  • Loading a plan
    • icm:loadplan – ”let’s see.”
  • Question accommodation
    • icm:accommodate – ”alright.”
  • Reraising an issue
    • icm:reraise – ”so, ”
    • icm:reraise:Q – ”returning to the issue of …”
sequencing icm reraising issue loading a plan
Sequencing ICM: reraising issue, loading a plan

Signals loading a plan

S> Welcome to the travel agency!

U> price information

S> (…) Lets see. How do you want to travel?

U> by flight

S> (…) What city do you want to go to?

U> paris

S> (…) What city do you want to go from?

U> do I need a visa ?

S> (…) Lets see. What country are you from?

U> sweden

S> Okay. Yes, you need a Visa.

S> Returning to the issue of price. Lets see. What city do you want to go from?

Reraising an issue explicitly

sequencing icm accommodation
Sequencing ICM: Accommodation

S> Lets see. What can I do for you?

U> channel five today

S> Alright. add a program… Lets see. Okay. channel five… today… What time do you want to start recording?

U> from six thirty to seven thirty

S> Okay. from six thirty… to seven thirty… The program has been added. ”

Signals accommodation

  • Current dialogue systems use only a fraction of the range of ICM used by humans
  • By extending the range of ICM used by systems, their communication becomes more natural and comprehensive
  • We have provided an initial classification of feedback and sequencing ICM useful in a dialogue system, and implemented it
  • (ICM can be correlated with updates to common ground, and used to signal these updates to the user)
future work
Future work
  • Replace current ad-hoc forms based on corpus studies and existing work
    • ”discourse markers” (Schiffrin)
    • ”cue phrases” (Grosz & Sidner and others)
  • ”Real” generation rather than templates
    • Integrate ICM with other utterances, rather than just appending them
  • Explore other ways of generating ICM
    • Intonation, facial expression, etc.
  • Extend the range of ICM generated (and interpreted) by the system
object or metalevel content
Object- or metalevel content
  • Utterances with metalevel content explicitly refer to contact, perception, understanding or acceptance
  • Object-level utterances instead refer to the task at hand
  • Example
    • S: What city are you going to?
    • U: Paris
    • S(1a): Did you say you’re going to Paris? [meta]
    • S(1b): Are you going to Paris? [object]
    • S(2a): Do you mean Paris, France or Paris, Texas?
    • S(2b): Do you want to go to Paris, France or Paris, Texas?
  • This dimension does not apply to all feedback, e.g. ”Paris.”, ”Pardon?”
  • (Is 2b feedback or simply an alternative question?)
realisation of feedback moves
Realisation of feedback moves
  • Syntactic form:
    • declarative: ”I didn’t hear what you said.”; ”The destination city is Paris.”
    • interrogative: ”What did you say?”; ”Do you want to go to Paris?”
    • imperative: ”Please repeat your latest utterance!”
    • elliptical
      • interrogative: ”Paris?”, ”To Paris or from Paris?”
      • declarative: ”To Paris.”
  • In general, the exact formulation of ICM phrases may depend on various contextual factors
    • including activity, noise level, time constraints etc.
correcting the system
Correcting the system

S> Lets see. What can I do for you?

U> add program

S> Okay. Add a program…

S> Lets see. What channel do you want?

U> channel six

S> OK, channel five.

U> no

S> not channel five. So, What channel do you want?

U> channel six

S> Okay. channel six.”


Interrogative feedback

Negative answer to fb.

Signals reraising an issue (Sequencing ICM)

reactions to positive and checking feedback
Reactions to positive and checking feedback
  • After positive feedback, system leaves a pause for the user to agree or disagree
    • if user gives no response, system assumes it was right
  • After check feedback, positive response is needed
    • or system will assume it was wrong
implicit feedback
Implicit feedback?
  • Clark: ”relevant followup” to U
    • What is relevant?
      • simple cases for followups to questions:
        • answer to question
        • ”subquestion”
        • feedback concering question
      • Complex cases: all other utterances
    • In general, complex inference and knowledge may be needed (implicatures)
    • Currently, irrelevant followup counts as negative feedback
  • What about no followup at all?
    • in reaction to ask-move or interrogative feedback, counts as negative
    • in reaction to answer or positive feedback, counts as positive

S: ”Where do you want to go?”

U1: ”Nowhere”

U2: ”I don’t know”

  • Should these count as rejections?
    • U1: negative answer? presupposition failiure? rejection?
    • U2: rejection?
      • but not as definite as ”No comment!”
relation to traum s computational theory of grounding
Relation to Traum’s computational theory of grounding
  • Focus on understanding-level
    • ”grounding” here refers only to the understanding level
    • Acceptance and rejection seen as ”core speech acts”
  • Focus on positive feedback and corrections (self and other)
    • Based on the TRAINS corpus of H-H dialogue
    • Deals with the question, when does a contribution end?
    • Corrections not included here; involves turntaking and OCM
  • Does not include sequencing ICM
godis an issue based dialogue system
GoDiS: an issue-based dialogue system
  • Explores and implements Issue-based dialogue management (Larsson 2002)
    • Based on Ginzburg’s notion of a dialogue gameboard involving Questions Under Discussion (QUD)
    • Uses (mostly pre-scripted) dialogue plans
  • Extends theory to more flexible dialogue
    • Multiple tasks, information sharing between tasks
    • Feedback and grounding
    • Question accommodation, re-raising, clarification
eliciting nonelciting feedback
Eliciting / nonelciting feedback
  • (Allwood et. al. 1992)
  • Eliciting feedback is intended to evoke a response from the user
  • Noneliciting feedback is not so intended
    • But may nevertheless recieve a response
simplifying assumptions regarding feedback
Simplifying assumptions regarding feedback
  • We only represent action level and polarity
  • In polarity, we replace ”neutral” by ”checking”
    • We exclude feedback which is neutral but not check-questions
  • Eliciting/noneliciting dimension implicit
    • Negative feedback is eliciting; since something went wrong, it must be fixed
    • Checking feedback is also eliciting, since it poses a question that must be adressed
    • Positive feedback is not eliciting (we assume)
  • Syntactic form not included
  • ”To ground a thing … is to establish it as part of common ground well enough for current purposes.” (Clark)
  • making sure that the participants are percieving, understanding, and accepting each other’s utterances