(hidden) information state models
DESCRIPTION
(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 PresentationTRANSCRIPT
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(Hidden) Information State Models
Ling575Discourse and Dialogue
May 25, 2011
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RoadmapInformation State Models
Dialogue ActsDialogue Act Recognition
Hidden Information State Models Learning dialogue behavior
Politeness and Speaking StyleGenerating styles
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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
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Information State Architecture
Simple ideas, complex execution
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Dialogue ActsExtension of speech acts
Adds structure related to conversational phenomenaGrounding, adjacency pairs, etc
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Dialogue ActsExtension of speech acts
Adds structure related to conversational phenomenaGrounding, adjacency pairs, etc
Many proposed tagsetsVerbmobil: acts specific to meeting sched domain
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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
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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
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Verbmobil DA18 high level tags
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Dialogue Act Interpretation
Automatically tag utterances in dialogueSome simple cases:
Will breakfast be served on USAir 1557?
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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.
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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
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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?
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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.
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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
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Dialogue Act AmbiguityIndirect speech acts
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Dialogue Act AmbiguityIndirect speech acts
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Dialogue Act AmbiguityIndirect speech acts
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Dialogue Act AmbiguityIndirect speech acts
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Dialogue Act AmbiguityIndirect speech acts
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Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:
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Dialogue Act RecognitionHow can we classify dialogue acts?Sources of information:
Word information: Please, would you: request; are you: yes-no question
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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:
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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
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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:
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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
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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
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Dialogue Act TagsetCover general conversational dialogue acts
No particular task/domain constraints
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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
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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)
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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
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Probabilistic Dialogue Models
HMM dialogue modelsStates = Dialogue acts; Observations: Utterances
Assume decomposable by utteranceEvidence from true words, ASR words, prosody
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Probabilistic Dialogue Models
HMM dialogue modelsStates = Dialogue acts; Observations: Utterances
Assume decomposable by utteranceEvidence from true words, ASR words, prosody
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Probabilistic Dialogue Models
HMM dialogue modelsStates = Dialogue acts; Observations: Utterances
Assume decomposable by utteranceEvidence from true words, ASR words, prosody
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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
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DA Classification - AllCombine word and prosodic information
Consider case with ASR words and acoustics
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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)
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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
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Integrated ClassificationFocused analysis
Prosodically disambiguated classesStatement/Question-Y/N and Agreement/BackchannelProsodic decision trees for agreement vs backchannel
Disambiguated by duration and loudness
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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
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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
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Many VariantsMaptask: (13 classes)
Serafin & DiEugenio 2004Latent Semantic analysis on utterance vectorsText onlyGame information; No improvement for DA history
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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
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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
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ObservationsDA classification can work on open domain
Exploits word model, DA context, prosodyBest results for prosody+wordsWords are quite effective alone – even ASR
Questions:
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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
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Detecting Correction ActsMiscommunication is common in SDS
Utterances after errors misrecognized >2x as oftenFrequently repetition or paraphrase of original input
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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
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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.
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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..
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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.
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Generating Dialogue ActsGeneration neglected relative to generation
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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
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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
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Generating ConfirmationSimple systems use fixed confirmation strategy
Implicit or explicitMore complex systems can select dynamically
Use information state and features to decide
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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
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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
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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:
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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
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Statistical Dialogue Management
Pioneered by Steve Young’s group at CambridgeModel dialogue as probabilistic agent
Markov Decision Process (MDP)
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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
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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?
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Dialogue StatesEncapsulate information about current dialogue
History:Everything (all states) so far?
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Dialogue StatesEncapsulate information about current dialogue
History:Everything (all states) so far?
Explosive
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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
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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:
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Actions & RewardsFor day, month input:
A1: question asking for day
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Actions & RewardsFor day, month input:
A1: question asking for dayA2: question asking for monthA3: question asking for day and monthA4: submitting the form
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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
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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
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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
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
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Learning PoliciesSimple system:
Can enumerate policies and select
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Learning PoliciesSimple system:
Can enumerate policies and selectComplex system:
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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
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Training the ModelState transition probabilities must be estimated
For small corpus
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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,
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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
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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
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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
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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..”
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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..”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”
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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”
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)
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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”
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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