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(Hidden) Information State Models Ling575 Discourse and Dialogue May 25, 2011

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(Hidden) Information State Models. Ling575 Discourse and Dialogue May 25, 2011. Roadmap. Information State Models Dialogue Acts Dialogue Act Recognition Hidden Information State Models Learning dialogue behavior Politeness and Speaking Style Generating styles. - PowerPoint PPT Presentation

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Page 1: (Hidden) Information State Models

(Hidden) Information State Models

Ling575Discourse and Dialogue

May 25, 2011

Page 2: (Hidden) Information State Models

RoadmapInformation State Models

Dialogue ActsDialogue Act Recognition

Hidden Information State Models Learning dialogue behavior

Politeness and Speaking StyleGenerating styles

Page 3: (Hidden) Information State Models

Information State SystemsInformation state :

Discourse context, grounding state, intentions, plans.Dialogue acts:

Extension of speech acts, to include grounding actsRequest-inform; Confirmation

Update rulesModify information state based on DAs

When a question is asked, answer itWhen an assertion is made,

Add information to context, grounding state

Page 4: (Hidden) Information State Models

Information State Architecture

Simple ideas, complex execution

Page 5: (Hidden) Information State Models

Dialogue ActsExtension of speech acts

Adds structure related to conversational phenomenaGrounding, adjacency pairs, etc

Page 6: (Hidden) Information State Models

Dialogue ActsExtension of speech acts

Adds structure related to conversational phenomenaGrounding, adjacency pairs, etc

Many proposed tagsetsVerbmobil: acts specific to meeting sched domain

Page 7: (Hidden) Information State Models

Dialogue ActsExtension of speech acts

Adds structure related to conversational phenomenaGrounding, adjacency pairs, etc

Many proposed tagsetsVerbmobil: acts specific to meeting sched domainDAMSL: Dialogue Act Markup in Several Layers

Forward looking functions: speech actsBackward looking function: grounding, answering

Page 8: (Hidden) Information State Models

Dialogue ActsExtension of speech acts

Adds structure related to conversational phenomenaGrounding, adjacency pairs, etc

Many proposed tagsetsVerbmobil: acts specific to meeting sched domainDAMSL: Dialogue Act Markup in Several Layers

Forward looking functions: speech actsBackward looking function: grounding, answering

Conversation acts:Add turn-taking and argumentation relations

Page 9: (Hidden) Information State Models

Verbmobil DA18 high level tags

Page 10: (Hidden) Information State Models

Dialogue Act Interpretation

Automatically tag utterances in dialogueSome simple cases:

Will breakfast be served on USAir 1557?

Page 11: (Hidden) Information State Models

Dialogue Act Interpretation

Automatically tag utterances in dialogueSome simple cases:

YES-NO-Q: Will breakfast be served on USAir 1557?

I don’t care about lunch.

Page 12: (Hidden) Information State Models

Dialogue Act Interpretation

Automatically tag utterances in dialogueSome simple cases:

YES-NO-Q: Will breakfast be served on USAir 1557?

Statement: I don’t care about lunch.Show be flights from L.A. to Orlando

Page 13: (Hidden) Information State Models

Dialogue Act Interpretation

Automatically tag utterances in dialogueSome simple cases:

YES-NO-Q: Will breakfast be served on USAir 1557?

Statement: I don’t care about lunch.Command: Show be flights from L.A. to Orlando

Is it always that easy?Can you give me the flights from Atlanta to

Boston?

Page 14: (Hidden) Information State Models

Dialogue Act Interpretation

Automatically tag utterances in dialogueSome simple cases:

YES-NO-Q: Will breakfast be served on USAir 1557?Statement: I don’t care about lunch.Command: Show be flights from L.A. to Orlando

Is it always that easy?Can you give me the flights from Atlanta to Boston?

Syntactic form: question; Act: request/commandYeah.

Page 15: (Hidden) Information State Models

Dialogue Act Interpretation

Automatically tag utterances in dialogueSome simple cases:

YES-NO-Q: Will breakfast be served on USAir 1557?Statement: I don’t care about lunch.Command: Show be flights from L.A. to Orlando

Is it always that easy?Can you give me the flights from Atlanta to Boston?Yeah.

Depends on context: Y/N answer; agreement; back-channel

Page 16: (Hidden) Information State Models

Dialogue Act AmbiguityIndirect speech acts

Page 17: (Hidden) Information State Models

Dialogue Act AmbiguityIndirect speech acts

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Dialogue Act AmbiguityIndirect speech acts

Page 19: (Hidden) Information State Models

Dialogue Act AmbiguityIndirect speech acts

Page 20: (Hidden) Information State Models

Dialogue Act AmbiguityIndirect speech acts

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Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:

Page 22: (Hidden) Information State Models

Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:

Word information: Please, would you: request; are you: yes-no question

Page 23: (Hidden) Information State Models

Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:

Word information: Please, would you: request; are you: yes-no questionN-gram grammars

Prosody:

Page 24: (Hidden) Information State Models

Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:

Word information: Please, would you: request; are you: yes-no questionN-gram grammars

Prosody:Final rising pitch: question; final lowering: statementReduced intensity: Yeah: agreement vs backchannel

Page 25: (Hidden) Information State Models

Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:

Word information: Please, would you: request; are you: yes-no questionN-gram grammars

Prosody:Final rising pitch: question; final lowering: statementReduced intensity: Yeah: agreement vs backchannel

Adjacency pairs:

Page 26: (Hidden) Information State Models

Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:

Word information: Please, would you: request; are you: yes-no questionN-gram grammars

Prosody:Final rising pitch: question; final lowering: statementReduced intensity: Yeah: agreement vs backchannel

Adjacency pairs:Y/N question, agreement vs Y/N question, backchannelDA bi-grams

Page 27: (Hidden) Information State Models

Task & CorpusGoal:

Identify dialogue acts in conversational speech

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Task & CorpusGoal:

Identify dialogue acts in conversational speechSpoken corpus: Switchboard

Telephone conversations between strangersNot task oriented; topics suggested1000s of conversations

recorded, transcribed, segmented

Page 29: (Hidden) Information State Models

Dialogue Act TagsetCover general conversational dialogue acts

No particular task/domain constraints

Page 30: (Hidden) Information State Models

Dialogue Act TagsetCover general conversational dialogue acts

No particular task/domain constraintsOriginal set: ~50 tags

Augmented with flags for task, conv mgmt220 tags in labeling: some rare

Page 31: (Hidden) Information State Models

Dialogue Act TagsetCover general conversational dialogue acts

No particular task/domain constraintsOriginal set: ~50 tags

Augmented with flags for task, conv mgmt220 tags in labeling: some rare

Final set: 42 tags, mutually exclusiveSWBD-DAMSLAgreement: K=0.80 (high)

Page 32: (Hidden) Information State Models

Dialogue Act TagsetCover general conversational dialogue acts

No particular task/domain constraintsOriginal set: ~50 tags

Augmented with flags for task, conv mgmt220 tags in labeling: some rare

Final set: 42 tags, mutually exclusiveSWBD-DAMSLAgreement: K=0.80 (high)

1,155 conv labeled: split into train/test

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Common TagsStatement & Opinion: declarative +/- opQuestion: Yes/No&Declarative: form, forceBackchannel: Continuers like uh-huh, yeahTurn Exit/Adandon: break off, +/- passAnswer : Yes/No, follow questionsAgreement: Accept/Reject/Maybe

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Probabilistic Dialogue Models

HMM dialogue models

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Probabilistic Dialogue Models

HMM dialogue modelsStates = Dialogue acts; Observations: Utterances

Assume decomposable by utteranceEvidence from true words, ASR words, prosody

Page 36: (Hidden) Information State Models

Probabilistic Dialogue Models

HMM dialogue modelsStates = Dialogue acts; Observations: Utterances

Assume decomposable by utteranceEvidence from true words, ASR words, prosody

Page 37: (Hidden) Information State Models

Probabilistic Dialogue Models

HMM dialogue modelsStates = Dialogue acts; Observations: Utterances

Assume decomposable by utteranceEvidence from true words, ASR words, prosody

Page 38: (Hidden) Information State Models

Probabilistic Dialogue Models

HMM dialogue modelsStates = Dialogue acts; Observations: Utterances

Assume decomposable by utteranceEvidence from true words, ASR words, prosody

Page 39: (Hidden) Information State Models

DA Classification - ProsodyFeatures:

Duration, pause, pitch, energy, rate, genderPitch accent, tone

Results:Decision trees: 5 common classes

45.4% - baseline=16.6%

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Prosodic Decision Tree

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DA Classification -WordsWords

Combines notion of discourse markers and collocations: e.g. uh-huh=Backchannel

Contrast: true words, ASR 1-best, ASR n-bestResults:

Best: 71%- true words, 65% ASR 1-best

Page 42: (Hidden) Information State Models

DA Classification - AllCombine word and prosodic information

Consider case with ASR words and acoustics

Page 43: (Hidden) Information State Models

DA Classification - AllCombine word and prosodic information

Consider case with ASR words and acousticsProsody classified by decision trees

Incorporate decision tree posteriors in model for P(f|d)

Page 44: (Hidden) Information State Models

DA Classification - AllCombine word and prosodic information

Consider case with ASR words and acousticsProsody classified by decision trees

Incorporate decision tree posteriors in model for P(f|d)

Slightly better than raw ASR

Page 45: (Hidden) Information State Models

Integrated ClassificationFocused analysis

Prosodically disambiguated classesStatement/Question-Y/N and Agreement/BackchannelProsodic decision trees for agreement vs backchannel

Disambiguated by duration and loudness

Page 46: (Hidden) Information State Models

Integrated ClassificationFocused analysis

Prosodically disambiguated classesStatement/Question-Y/N and Agreement/BackchannelProsodic decision trees for agreement vs backchannel

Disambiguated by duration and loudness

Substantial improvement for prosody+wordsTrue words: S/Q: 85.9%-> 87.6; A/B: 81.0%->84.7

Page 47: (Hidden) Information State Models

Integrated ClassificationFocused analysis

Prosodically disambiguated classesStatement/Question-Y/N and Agreement/BackchannelProsodic decision trees for agreement vs backchannel

Disambiguated by duration and loudness

Substantial improvement for prosody+wordsTrue words: S/Q: 85.9%-> 87.6; A/B: 81.0%->84.7ASR words: S/Q: 75.4%->79.8; A/B: 78.2%->81.7

More useful when recognition is iffy

Page 48: (Hidden) Information State Models

Many VariantsMaptask: (13 classes)

Serafin & DiEugenio 2004Latent Semantic analysis on utterance vectorsText onlyGame information; No improvement for DA history

Page 49: (Hidden) Information State Models

Many VariantsMaptask: (13 classes)

Serafin & DiEugenio 2004Latent Semantic analysis on utterance vectorsText onlyGame information; No improvement for DA history

Surendran & Levow 2006SVMs on term n-grams, prosodyPosteriors incorporated in HMMs

Prosody, sequence modeling improves

Page 50: (Hidden) Information State Models

Many VariantsMaptask: (13 classes)

Serafin & DiEugenio 2004Latent Semantic analysis on utterance vectorsText onlyGame information; No improvement for DA history

Surendran & Levow 2006SVMs on term n-grams, prosodyPosteriors incorporated in HMMs

Prosody, sequence modeling improves

MRDA: Meeting tagging: 5 broad classes

Page 51: (Hidden) Information State Models

ObservationsDA classification can work on open domain

Exploits word model, DA context, prosodyBest results for prosody+wordsWords are quite effective alone – even ASR

Questions:

Page 52: (Hidden) Information State Models

ObservationsDA classification can work on open domain

Exploits word model, DA context, prosodyBest results for prosody+wordsWords are quite effective alone – even ASR

Questions: Whole utterance models? – more fine-grainedLonger structure, long term features

Page 53: (Hidden) Information State Models

Detecting Correction ActsMiscommunication is common in SDS

Utterances after errors misrecognized >2x as oftenFrequently repetition or paraphrase of original input

Page 54: (Hidden) Information State Models

Detecting Correction ActsMiscommunication is common in SDS

Utterances after errors misrecognized >2x as oftenFrequently repetition or paraphrase of original input

Systems need to detect, correct

Page 55: (Hidden) Information State Models

Detecting Correction ActsMiscommunication is common in SDS

Utterances after errors misrecognized >2x as oftenFrequently repetition or paraphrase of original input

Systems need to detect, correctCorrections are spoken differently:

Hyperarticulated (slower, clearer) -> lower ASR conf.

Page 56: (Hidden) Information State Models

Detecting Correction ActsMiscommunication is common in SDS

Utterances after errors misrecognized >2x as oftenFrequently repetition or paraphrase of original input

Systems need to detect, correctCorrections are spoken differently:

Hyperarticulated (slower, clearer) -> lower ASR conf.

Some word cues: ‘No’,’ I meant’, swearing..

Page 57: (Hidden) Information State Models

Detecting Correction ActsMiscommunication is common in SDS

Utterances after errors misrecognized >2x as oftenFrequently repetition or paraphrase of original input

Systems need to detect, correctCorrections are spoken differently:

Hyperarticulated (slower, clearer) -> lower ASR conf.

Some word cues: ‘No’,’ I meant’, swearing..Can train classifiers to recognize with good acc.

Page 58: (Hidden) Information State Models

Generating Dialogue ActsGeneration neglected relative to generation

Page 59: (Hidden) Information State Models

Generating Dialogue ActsGeneration neglected relative to generationStent (2002) model: Conversation acts, Belief

modelDevelops update rules for content planning, e.g.

If user releases turn, system can do ‘TAKE-TURN’ actIf system needs to summarize, use ASSERT act

Page 60: (Hidden) Information State Models

Generating Dialogue ActsGeneration neglected relative to generationStent (2002) model: Conversation acts, Belief

modelDevelops update rules for content planning, i.e.

If user releases turn, system can do ‘TAKE-TURN’ actIf system needs to summarize, use ASSERT act

Identifies turn-taking as key aspect of dialogue gen.

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Generating ConfirmationSimple systems use fixed confirmation strategy

Implicit or explicit

Page 62: (Hidden) Information State Models

Generating ConfirmationSimple systems use fixed confirmation strategy

Implicit or explicitMore complex systems can select dynamically

Use information state and features to decide

Page 63: (Hidden) Information State Models

Generating ConfirmationSimple systems use fixed confirmation strategy

Implicit or explicitMore complex systems can select dynamically

Use information state and features to decideLikelihood of error:

Low ASR confidence score If very low, can reject

Page 64: (Hidden) Information State Models

Generating ConfirmationSimple systems use fixed confirmation strategy

Implicit or explicitMore complex systems can select dynamically

Use information state and features to decideLikelihood of error:

Low ASR confidence score If very low, can reject

Sentence/prosodic features: longer, initial pause, pitch range

Page 65: (Hidden) Information State Models

Generating ConfirmationSimple systems use fixed confirmation strategy

Implicit or explicitMore complex systems can select dynamically

Use information state and features to decideLikelihood of error:

Low ASR confidence score If very low, can reject

Sentence/prosodic features: longer, initial pause, pitch range

Cost of error:

Page 66: (Hidden) Information State Models

Generating ConfirmationSimple systems use fixed confirmation strategy

Implicit or explicitMore complex systems can select dynamically

Use information state and features to decideLikelihood of error:

Low ASR confidence score If very low, can reject

Sentence/prosodic features: longer, initial pause, pitch rangeCost of error:

Book a flight vs looking up information

Markov Decision Process models more detailed

Page 67: (Hidden) Information State Models

Statistical Dialogue Management

Pioneered by Steve Young’s group at CambridgeModel dialogue as probabilistic agent

Markov Decision Process (MDP)

Page 68: (Hidden) Information State Models

Statistical Dialogue Management

Pioneered by Steve Young’s group at CambridgeModel dialogue as probabilistic agent

Markov Decision Process (MDP)Characterized by:

S: set of states agent can be inA: set of actions the agent can take R(a,s): reward agent gets for action a in state s

Page 69: (Hidden) Information State Models

Statistical Dialogue Management

Pioneered by Steve Young’s group at CambridgeModel dialogue as probabilistic agent

Markov Decision Process (MDP)Characterized by:

S: set of states agent can be in A: set of actions the agent can take R(a,s): reward agent gets for action a in state s

Learn: Π: Policy: Which action a should agent in state s take

to achieve highest reward?

Page 70: (Hidden) Information State Models

Dialogue StatesEncapsulate information about current dialogue

History:Everything (all states) so far?

Page 71: (Hidden) Information State Models

Dialogue StatesEncapsulate information about current dialogue

History:Everything (all states) so far?

Explosive

Page 72: (Hidden) Information State Models

Dialogue StatesEncapsulate information about current dialogue

History:Everything (all states) so far?

ExplosiveMarkov assumptions

Typically:Value of current frame slots, Most recent system

questionMost recent user answer, ASR confidence, etc

Page 73: (Hidden) Information State Models

Dialogue StatesEncapsulate information about current dialogue

History:Everything (all states) so far?

ExplosiveMarkov assumptions

Typically:Value of current frame slots, Most recent system

questionMost recent user answer, ASR confidence, etc

For day, month frame: 411 states!

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Actions & RewardsFor day, month input:

Page 75: (Hidden) Information State Models

Actions & RewardsFor day, month input:

A1: question asking for day

Page 76: (Hidden) Information State Models

Actions & RewardsFor day, month input:

A1: question asking for dayA2: question asking for monthA3: question asking for day and monthA4: submitting the form

Page 77: (Hidden) Information State Models

Actions & RewardsFor day, month input:

A1: question asking for dayA2: question asking for monthA3: question asking for day and monthA4: submitting the form

Reward:Correct answer with shortest interactionR = (wini+wcnc+wfnf)

Ni:# interactions; nc:# errors; nf: # filled slots

Page 78: (Hidden) Information State Models

Policies1) Asking for Day, Month together2) Asking for Day, Month separatelyCompute reward for each policy, given some

P(error)

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UtilityA utility function

maps a state or state sequence onto a real number describing the goodness of that state I.e. the resulting “happiness” of the agent

Speech and Language Processing -- Jurafsky and Martin

Page 80: (Hidden) Information State Models

UtilityA utility function

maps a state or state sequence onto a real number describing the goodness of that state I.e. the resulting “happiness” of the agent

Principle of Maximum Expected Utility:A rational agent should choose an action that

maximizes the agent’s expected utility

Speech and Language Processing -- Jurafsky and Martin

Page 81: (Hidden) Information State Models

Learning PoliciesSimple system:

Can enumerate policies and select

Page 82: (Hidden) Information State Models

Learning PoliciesSimple system:

Can enumerate policies and selectComplex system:

Page 83: (Hidden) Information State Models

Learning PoliciesSimple system:

Can enumerate policies and selectComplex system:

Huge number of actions, states, policiesSelection is complex optimization problemCan describe expected cumulative reward w/Bellman

eqnStandard approach in reinforcement learningSolvable with value iteration algorithm

Page 84: (Hidden) Information State Models

Training the ModelState transition probabilities must be estimated

For small corpus

Page 85: (Hidden) Information State Models

Training the ModelState transition probabilities must be estimated

For small corpus Get real users for systemCompute results for different choices (i.e. initiative)Directly collect empirical estimate

For larger system,

Page 86: (Hidden) Information State Models

Training the ModelState transition probabilities must be estimated

For small corpus Get real users for systemCompute results for different choices (i.e. initiative)Directly collect empirical estimate

For larger system, too many alternativesNeed arbitrary number of users

Page 87: (Hidden) Information State Models

Training the ModelState transition probabilities must be estimated

For small corpus Get real users for systemCompute results for different choices (i.e. initiative)Directly collect empirical estimate

For larger system, too many alternativesNeed arbitrary number of users Simulation!!Stochastic state selection

Page 88: (Hidden) Information State Models

Training the ModelState transition probabilities must be estimated

For small corpus Get real users for systemCompute results for different choices (i.e. initiative)Directly collect empirical estimate

For larger system, too many alternativesNeed arbitrary number of users Simulation!!Stochastic state selection

Learned policies can outperform hand-crafted

Page 89: (Hidden) Information State Models

Politeness & Speaking Style

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AgendaMotivationExplaining politeness & indirectness

Face & rational reasoningDefusing Face Threatening Acts

Selecting & implementing speaking stylesPlan-based speech act modelingSocially appropriate speaking styles

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Why be Polite to Computers?

Computers don’t have feelings, status, etc

Would people be polite to a machine?

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Why be Polite to Computers?

Computers don’t have feelings, status, etc

Would people be polite to a machine?Range of politeness levels:Direct < Hinting < Conventional Indirectness

Why?

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Varying PolitenessDirect Requests:

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Varying PolitenessDirect Requests:

Read it to meGo to the next groupNext message

Polite Requests: Conventional Indirectness

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Varying PolitenessDirect Requests:

Read it to meGo to the next groupNext message

Polite Requests: Conventional IndirectnessI’d like to check Nicole’s calendarCould I have the short term forecast for Boston?Weather please

Goodbye spirals

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Why are People Polite to Each Other?

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Why are People Polite to Each Other?

“Convention”Begs the question - why become convention?

Indirectness

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Why are People Polite to Each Other?

“Convention”Begs the question - why become convention?

IndirectnessNot just adding as many hedges as possible

“Could someone maybe please possibly be able to..”

Page 99: (Hidden) Information State Models

Why are People Polite to Each Other?

“Convention”Begs the question - why become convention?

IndirectnessNot just adding as many hedges as possible

“Could someone maybe please possibly be able to..”Social relation and rational agency

Maintaining face, rational reasoningPragmatic clarity

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FaceKernel of politeness

Cross-culturalPublic self-image

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FaceKernel of politeness

Cross-culturalPublic self-image

Negative: Claim of freedom to action, from imposition “Want” to be unimpeded by others: “Autonomy”

Page 102: (Hidden) Information State Models

FaceKernel of politeness

Cross-culturalPublic self-image

Negative: Claim of freedom to action, from imposition “Want” to be unimpeded by others: “Autonomy”

Positive: Desire to be approved of“Want” to be liked - usually by specific people for specific attr

Generally cooperate to preserve faceMutually vulnerable

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Threatening & Saving FaceCommunicative acts may threaten face

Negative:

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Threatening & Saving FaceCommunicative acts may threaten face

Negative: Put pressure on H to do, acceptE.g. request, suggest, remind, offer, compliment,..

Positive

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Threatening & Saving FaceCommunicative acts may threaten face

Negative: Put pressure on H to do, acceptE.g. request, suggest, remind, offer, compliment,..

Positive: Indicate dislike or indifference to faceE.g. criticism, disapproval, contradiction, boasting

Threats to H’s or S’s face; positive/negative

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Threatening & Saving FaceCommunicative acts may threaten face

Negative: Put pressure on H to do, acceptE.g. request, suggest, remind, offer, compliment,..

Positive: Indicate dislike or indifference to faceE.g. criticism, disapproval, contradiction, boasting

Threats to H’s or S’s face; positive/negativeGiven threats, rational agents will

minimizeConstraints: communicate content, be

efficient, maintain H’s face

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How to be PoliteOn-record: with clear intent

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How to be PoliteOn-record: with clear intent

Without redress, baldly:Direct: clear and concise as possible

Very casual or very urgent

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How to be PoliteOn-record: with clear intent

Without redress, baldly:Direct: clear and concise as possible

Very casual or very urgentWith redress, positive:

Indicate S want H’s wants

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How to be PoliteOn-record: with clear intent

Without redress, baldly:Direct: clear and concise as possible

Very casual or very urgentWith redress, positive:

Indicate S want H’s wantsWith redress, negative: avoidance-based

Conventional indirectness

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How to be PoliteOn-record: with clear intent

Without redress, baldly:Direct: clear and concise as possible

Very casual or very urgentWith redress, positive:

Indicate S want H’s wantsWith redress, negative: avoidance-based

Conventional indirectness

Off-record: ambiguous intent - hintDon’t ask….

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Indirectness vs PolitenessPoliteness not just maximal indirectness

Not just maintain faceBalance minimizing inferential effort If too indirect, inferential effort high

E.g. hinting viewed as impolite

Conventionalized indirectness eases interpMaintain face and pragmatic clarity

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Generating Speaking Styles

Stylistic choicesSemantic content, syntactic form, acoustic realiz’nLead listeners to make inferences about character

and personalityBase on:

Speech ActsSocial Interaction & Linguistic Style

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Dialogue Act ModelingSmall set of basic communicative intents

Initiating: Inform, offer, request-info, request-actResponse: Accept or reject: offer, request, act

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Dialogue Act ModelingSmall set of basic communicative intents

Initiating: Inform, offer, request-info, request-actResponse: Accept or reject: offer, request, act

Distinguish: intention of act from realizationAbstract representation for utterances

Each utterance instantiates plan operator

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Dialogue Act ModelPlan-based speech act decompositionSpeech Act defined as plan

Header: request-act(s,h,a)Precondition: want(s,a), cando(h,a)Effects: want(h,a), know(h,want(s,a))Decompositions

Different alternatives specify surface realization Select based on social information

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Decomposition & Realization

Surface-request(s,h,a) “Do a”.

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Decomposition & Realization

Surface-request(s,h,a) “Do a”.

Surface-request(s,h,informif(h,s,cando(h,a))) “Can you do a?”

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Decomposition & Realization

Surface-request(s,h,a) “Do a”.

Surface-request(s,h,informif(h,s,cando(h,a))) “Can you do a?”

Surface-request(s,h,~cando(s,a)) “I can’t do a”

Surface-request(s,h,want(s,a)) “I want you to do a.”

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Representing the Script(Manually) Model sequence in story/task

Sequence of dialogue acts and physical actsModel world, domain, domain plans

Preconditions, effects, decompositions=> semantic content

Represent as input to linguistic realizer

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Modeling Social Interaction

Based on B&L model of speakersFace: Autonomy and Approval; Rational meaning

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Modeling Social Interaction

Based on B&L model of speakersFace: Autonomy and Approval; Rational meaning

Based strategy on socially determined varsSocial distance, Power, Ranking of Imposition: 1-

50 Requests, offer, inform: threat to auto; rejects: threat to

approval

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Modeling Social Interaction

Based on B&L model of speakersFace: Autonomy and Approval; Rational meaning

Based strategy on socially determined varsSocial distance, Power, Ranking of Imposition: 1-50

Requests, offer, inform: threat to auto; rejects: threat to approval

Try to avoid threats to faceTheta= social distance + power + impositionSelect strategies based on theta:

Direct < Approval-oriented < Autonomy-oriented<off-rec

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Modeling Social Interaction

Based on B&L model of speakersFace: Autonomy and Approval; Rational meaning

Based strategy on socially determined varsSocial distance, Power, Ranking of Imposition: 1-50

Requests, offer, inform: threat to auto; rejects: threat to approval

Try to avoid threats to faceTheta= social distance + power + impositionSelect strategies based on theta:

Direct < Approval-oriented < Autonomy-oriented<off-rec

Semantic content: plan rep; syntactic form: library

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Modeling Social Interaction

Based on B&L model of speakers Face: Autonomy and Approval; Rational meaning

Based strategy on socially determined vars Social distance, Power, Ranking of Imposition: 1-50

Requests, offer, inform: threat to auto; rejects: threat to approval

Try to avoid threats to face Theta= social distance + power + imposition Select strategies based on theta:

Direct < Approval-oriented < Autonomy-oriented<off-rec

Semantic content: plan rep; syntactic form: libraryAffect: set acoustic realization

Angry, pleasant, disgusted, annoyed, distraught, sad, gruff

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Generating Appropriate Style

Input: Sequence of speech actsSocial status: social distance, power, rankingEmotional stance (view as orthogonal)

Page 127: (Hidden) Information State Models

Generating Appropriate Style

Input: Sequence of speech actsSocial status: social distance, power, rankingEmotional stance (view as orthogonal)

Example: Speech act= request;Status: D+P+R < 50

Direct: Imperative form: “Bring us two drinks”

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Generating Appropriate Style

Input: Sequence of speech actsSocial status: social distance, power, rankingEmotional stance (view as orthogonal)

Example: Speech act= request;Status: D+P+R < 50

Direct: Imperative form: “Bring us two drinks”

Status: 91<D+P+R<120Autonomy-oriented: query-capability-autonomy

“Can you bring us two drinks?” - Conventional indirect

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Controlling AffectAffect editor (Cahn 1990)Input: POS, phrase boundaries, focusAcoustic parameters: Vary from neutral

17: pitch, timing, voice and phoneme qualityPrior evaluation:

Naïve listeners reliably assign to affect class

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SummaryPoliteness and speaking style

Rational agent maintaining face, clarityIndirect requests allow hearer to save face

Must be clear enough to interpretSensitive to power and social relationships

Generate appropriate style based onDialogue acts (domain-specific plans)Define social distance and powerEmotional state