04/19/23 1

Dialogue Acts

Julia Hirschberg

LSA07 353

04/19/23 2

Today

• Recognizing structural information: Dialogue Acts vs. Discourse Structure

• Speech Acts Dialogue Acts– Coding schemes (DAMSL)– Practical goals

• Identifying DAs– Direct and indirect DAs: experimental results– Corpus studies of DA disambiguation– Automatic DA identification– More corpus studies

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Speech Acts

• Wittgenstein ’53, Austin ’62 and Searle ’75• Contributions to dialogue are actions performed

by speakers:– I promise to make you very very sorry for that.– Performative verbs

• Locutionary act: the act of conveying the ‘meaning’ of the sentence uttered (e.g. committing the Speaker to making the hearer sorry)

• Ilocutionary act: the act associated with the verb uttered (e.g. promising)

• Perlocutionary act: the act of producing an effect on the Hearer (e.g. threatening)

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Searle’s Classification Scheme

• Assertives: commit S to the truth of X (e.g. The world is flat)

• Directives: attempt by S to get H to do X (e.g. Open the window please)

• Commissives: commit S to do X (e.g. I’ll do it tomorrow)

• Expressives: S’s description of his/her own feelings about X (e.g. I’m sorry I screamed)

• Declarations: S brings about a change in the world by virtue of uttering X (e.g. I divorce you, I resign)

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Dialogue Acts

• Roughly correspond to Illocutionary acts– Motivation: Modeling Spoken Dialogue– Many coding schemes (e.g. DAMSL)– Many-to-many mapping between DAs and words

• Agreement DA can realized by Okay, Um, Right, Yeah, …• But each of these can express multiple DAs, e.g.

S: You should take the 10pm flight.

U: Okay

…that sounds perfect.

…but I’d prefer an earlier flight.

…(I’m listening)

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A Possible Coding Scheme for ‘ok’

• Ritualistic?– Closing – You're welcome – Other – No

• 3rd-Turn-Receipt?– Yes – No

• If Ritualistic==No, code all of these as well:

• Task Management:– I'm done– I'm not done yet– None

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• Topic Management:– Starting new topic– Finished old topic– Pivot: finishing and starting

• Turn Management:– Still your turn (=traditional backchannel)– Still my turn (=stalling for time)

– I'm done, it is now your turn

– None

• Belief Management:– I accept your proposition – I entertain your proposition– I reject your proposition– Do you accept my proposition? (=ynq)– None

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Practical Goals

• In Spoken Dialogue Systems– Disambiguate current DA

• Represent user input correctly• Respond appropriately

– Predict next DA• Switch Language Models for ASR• Switch states in semantic processing

– Produce DA for next system turn appropriately

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Disambiguating Ambiguous DAs Intonationally

• Modal (Can/would/would..willing) questions– Can you move the piano?– Would you move the piano?– Would you be willing to move the piano?

• Nickerson & Chu-Carroll ’99: Can info-requests be disambiguated reliably from action-requests?– By prosodic information?– Role of politeness

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Production Studies

• Design– Subjects read ambiguous questions in disambiguating

contexts– Control for given/new and contrastiveness– Polite/neutral/impolite readings– ToBI-style labeling

• Problems:– Cells imbalanced; little data– No pretesting– No distractors– Same speaker reads both contexts– No perception checks

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Results

• Indirect requests (e.g. for action)– If L%, more likely (73%) to be indirect– If H%,46% were indirect: differences in height

of boundary tone?– Politeness: can differs in impolite (higher rise)

vs. neutral cases– Speaker variability

• Some production differences– Limited utility in production of indirect DAs– Beware too steep a rise

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Corpus Studies: Jurafsky et al ‘98

• Can we distinguish different DA functions for affirmative words– Lexical, acoustic/prosodic/syntactic

differentiators for yeah, ok, uhuh, mhmm, um…

– Functional categories to distinguish• Continuers: Mhmm (not taking floor)• Assessments: Mhmm (tasty)• Agreements: Mhmm (I agree)• Yes answers: Mhmm (That’s right)• Incipient speakership: Mhmm (taking floor)

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Questions

• Are these terms important cues to dialogue structure?

• Does prosodic variation help to disambiguate them?

• Is there any difference in syntactic realization of certain DAs, compared to others?

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Corpus

• SwitchBoard telephone conversation corpus– Hand segmented and labeled with DA

information (initially from text) using the SWBD-DAMSL dialogue tagset

• ~60 labels that could be combined in different dimensions

– 84% inter-labeler agreement on tags– Tagset reduced to 42

• 7 CU-Boulder linguistics grad students labeling switchboard conversations of human-to-human interaction

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– Relabeling from speech only 2% changed labels (114/5757)

• 43/987 continuers --> agreements• Why?

– Shorter duration, lower F0, lower energy, longer preceding pause

– DAs analyzed for• Lexical realization• F0 and intensity features• Syntactic patterns

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Results: Lexical Differences

• Agreements– yeah (36%), right (11%),...

• Continuer– uhuh (45%), yeah (27%),…

• Incipient speaker– yeah (59%), uhuh (17%), right (7%),…

• Yes-answer– yeah (56%), yes (17%), uhuh (14%),...

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Prosodic and Lexico/Syntactic Cues

• Over all DA’s, duration best differentiator – Highly correlated with DA length in words

• Assessments: – Pro Term + Copula + (Intensifier) +

Assessment Adjective– That’s X (good, great, fine,…)

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Observations

• Yeah (and variations) ambiguous – agreement at 36% – incipient speaker at 59%– Yes-answer at 86%

• Uh-huh (with its variations): – a continuer at 45% (vs. yeah at 27%)

• Continuers (compared to agreements) are:– shorter in duration– less intonationally `marked’ – Preceded by longer pauses

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Hypothesis

• Prosodic information may be particularly helpful in distinguishing DAs with less lexical content

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Automatic DA Detection

• Rosset & Lamel ’04: Can we detect DAs automatically w/ minimal reliance on lexical content?– Lexicons are domain-dependent– ASR output is errorful

• Corpora (3912 utts total)– Agent/client dialogues in a French bank call

center, in a French web-based stock exchange customer service center, in an English bank call center

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• DA tags (44) similar to DAMSL– Conventional (openings, closings)– Information level (items related to the semantic

content of the task)– Forward Looking Function:

• statement (e.g. assert, commit, explanation)• infl on Hearer (e.g. confirmation, offer, request)

– Backward Looking Function: • Agreement (e.g. accept, reject)• Understanding (e.g. backchannel, correction)

– Communicative Status (e.g. self-talk, change-mind)

– NB: each utt could receive a tag for each class, so utts represented as vectors

• But…only 197 combinations observed

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– Method: Memory-based learning (TIMBL)• Uses all examples for classification• Useful for sparse data

– Features• Speaker identity• First 2 words of each turn• # utts in turn• Previously proposed DA tags for utts in turn

– Results• With true utt boundaries:

– ~83% accuracy on test data from same domain– ~75% accuracy on test data from different domain

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– On automatically identified utt units: 3.3% ins, 6.6% del, 13.5% sub

• Which DAs are easiest/hardest to detect?

DA GE.fr CAP.fr GE.eng

Resp-to 52.0% 33.0% 55.7%

Backch 75.0% 72.0% 89.2%

Accept 41.7% 26.0% 30.3%

Assert 66.0% 56.3% 50.5%

Expression 89.0% 69.3% 56.2%

Comm-mgt 86.8% 70.7% 59.2%

Task 85.4% 81.4% 78.8%

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• Conclusions– Strong ‘grammar’ of DAs in Spoken Dialogue

systems– A few initial words perform as well as more

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Phonetic, Prosodic, and Lexical Context Cues to DA Disambiguation

• Hypothesis: Prosodic information may be important for disambiguating shorter DAs

• Observation: ASR errors suggest it would be useful to limit the role of lexical content in DA disambiguation as much as possible …and that this is feasible

• Experiment:– Can people distinguish one (short) DA from

another purely from phonetic/acoustic/prosodic cues?

– Are they better with lexical context?

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The Columbia Games CorpusCollection

• 12 spontaneous task-oriented dyadic conversations in Standard American English.

• 2 subjects playing a computer game, no eye contact.

Describer: Follower:

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The Columbia Games CorpusAffirmative Cue Words

Cue Words– alright– gotcha– huh– mm-hm– okay– right– uh-huh– yeah– yep– yes– yup

Functions

– Acknowledgment / Agreement

– Backchannel

– Cue beginning discourse segment

– Cue ending discourse segment

– Check with the interlocutor

– Stall / Filler

– Back from a task

– Literal modifier

– Pivot beginning

– Pivot ending

count1. the

45652. of

15343. okay

11514. and

8865. like

753 …

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Perception StudySelection of Materials

– okay Speaker 1: but it's gonna be below the onionSpeaker 2: okay

– Cue beginning discourse segment– Backchannel

– Acknowledgment / Agreement

Speaker 1: okay alright I'll try it okaySpeaker 2: okay the owl is blinking

Speaker 1: yeah um there's like there's some space there'sSpeaker 2: okay I think I got it

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contextualized ‘okay’

Perception StudyExperiment Design

• 54 instances of ‘okay’ (18 for each function).• 2 tokens for each ‘okay’:• Isolated condition: Only the word ‘okay’.• Contextualized condition: 2 full speaker turns:

– The turn containing the target ‘okay’; and– The previous turn by the other speaker.

speakers okayokay

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Perception StudyExperiment Design

• Two conditions:– Part 1: 54 isolated tokens– Part 2: 54 contextualized tokens

• Subjects asked to classify each token of ‘okay’ as:– Acknowledgment / Agreement, or– Backchannel, or – Cue beginning discourse segment.

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Perception StudyDefinitions Given to the Subjects

• Acknowledge/Agreement:– The function of okay that indicates “I believe

what you said” and/or “I agree with what you say”.

• Backchannel:– The function of okay in response to another

speaker's utterance that indicates only “I’m still here” or “I hear you and please continue”.

• Cue beginning discourse segment– The function of okay that marks a new segment

of a discourse or a new topic. This use of okay could be replaced by now.

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Perception StudySubjects and Procedure

• Subjects:– 20 paid subjects (10 female, 10 male).– Ages between 20 and 60.– Native speakers of English.– No hearing problems.

• GUI on a laboratory workstation with headphones.

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ResultsInter-Subject Agreement

• Kappa measure of agreement with respect to chance (Fleiss ’71)

Isolated Condition Contextualized Condition

Overall .120 .294

Ack / Agree vs. Other .089 .227

Backchannel vs. Other .118 .164

Cue beginning vs. Other .157 .497

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ResultsCues to Interpretation

• Phonetic transcription of okay:

• Isolated Condition

Strong correlation for realization of initial vowel

Backchannel

Ack/Agree, Cue Beginning

• Contextualized Condition

No strong correlations found for phonetic variants.

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ResultsCues to Interpretation

Isolated Condition Contextualized Condition

Ack / Agree

Shorter /k/ Shorter latency between turns

Shorter pause before okay

Backchannel

Higher final pitch slope

Longer 2nd syllable

Lower intensity

Higher final pitch slope

More words by S2 before okay

Fewer words by S1 after okay

Cue beginning

Lower final pitch slope

Lower overall pitch slope

Lower final pitch slope

Longer latency between turns

More words by S1 after okay

S1 = Utterer of the target ‘okay’. S2 = The other speaker.

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Conclusions

• Agreement:– Availability of context improves inter-subject

agreement.– Cue beginnings easier to disambiguate than

the other two functions.• Cues to interpretation:

– Contextual features override word features– Exception: Final pitch slope of okay in both

conditions.• Guide to generation…

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Summary: Dialogue Act Modeling for SDS

• DA identification– Looks potentially feasible, even when

transcription is errorful– Prosodic and lexical cues useful

• DA generation– Descriptive results may be more useful for

generation than for recognition, ironically– Choice of DA realization, lexical and prosodic

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Next Class

• J&M 22.5• Hirschberg et al ’04• Goldberg et al ’03• Krahmer et al ‘01