A Question of Questions: Prosodic Cues to Question Form and Function Julia Hirschberg (Joint work with) Jennifer Venditti and Jackson Liscombe

Questioning in Dialogue A fundamental activity in conversation Elicit information Elicit action But How to define a question? Bolinger ’57: “fundamentally an attitude…an utterance that ‘craves’ a verbal or other semiotic … response” Ginzburg & Sag ‘00: “the semantic object associated with the attitude of wondering and the speech act of questioning” How to identify a question as such How to represent its semantics? The intention of the questioner?

Distinguishing Question Form and Function Questions may take many syntactic forms Is it a question? What is a question? It’s a question, isn’t it? Is it a question or an answer? Right? It’s a question? Questions may serve many pragmatic functions Clarification-seeking? Information-seeking? Confirmation- seeking? Possible Indicators Syntactic cues Context Intonation

Questions in Spoken Dialogue Systems Goals Examine question form and function How are they related? What features characterize them? Identify form and function automatically in an Intelligent Tutoring domain

Previous Studies Integration of prosodic tree model with language model based on words yields best performance accuracy in detecting questions/question form (Shriberg et al.’98: English) Some corpus-based (MapTask) studies have examined tune/accent types wrt. question function (Kowtko’96: Glaswegian English; Grice et al.’95: German, Italian, Bulgarian) Studies of different types (functions) of clarification questions (Rodríguez & Schlangen’94: German; Edlund et al.’95: Swedish) Our goal: a comprehensive quantitative analysis of question form and function in English which will permit question form/function identification

Domain: Intelligent Tutoring Systems ITSs must be able to recognize both the form and function of student questions Students ask human tutors many questions More questions  better learning Different question FORMs seek different information e.g. polar questions seek yes-no answer wh-questions seek different information Different question FUNCTIONs also often require different types of answers

Wh-questions, e.g. Information-seeking: (S has just submitted an essay to the tutor) S: Ok, what do you think about that? T: Uh, well that uh you have uh there are too many parameters here which uh need definition... Clarification-seeking: T: So if there is if the only force on an object in earth’s gravity then what is its motion called? S: What was the motion called? T: Yes, what’s the name for this motion?

Yes-no questions, e.g. Information-seeking  tutor provides additional information Clarification  clarification subdialogue Successful ITSs must be able to recognize the presence of a question in a student turn and its form and function

Question Corpus Human-human tutoring dialogs collected by Litman et al.’04 for development of ITSpoke, a speech-enabled ITS designed to teach physics Why2-Atlas ( Kurt VanLehn (U. Pitt), Art Graesser (U. Memphis)) Corpus includes 1030 student questions ‘Question’ defined a la Bolinger ‘57 as “an utterance that craves a response” 25.2 Qs/hour 13.3% of total student speaking time This study: a subset of 643 tokens

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Question Detection what symbol are you talking about do i have to rewrite this again am i ok with that so it’d be one meter per second squared

Coding question type Form coding based on surface syntax Declarative question (dQ): It’s a vector? A vector? Yes-no question (ynQ): Is it a vector? Wh-question (whQ): What is a vector? Tag question (ynTAG): It’s a vector, isn’t it? Alternative question (altQ): Is it a vector or a scalar? Particle (part): Huh? Function coding derived from Stenström ‘84 Confirmation-seeking check question (chk) Clarification-seeking question (clar) Information-seeking question (info) Other (oth)

Form/Function Distribution chkclarinfoothN (%) dQ (53.5) ynQ (25.7) whQ (10.6) ynTAG (7.2) altQ (1.9) part-8--8 (1.2) N (%)(55.5)(35.1)(7.9)(1.4)(100)

Falling (L-L%) F0 contours chkclarinfoothN (%) dQ34--7 (2.0) ynQ (6.7) whQ (42.6) ynTAG11--2 (4.3) altQ251-8 (66.7) part----- N (%)(1.7)(11.5)(45.1)(22.2)(100)

F0 measures of non-falling questions Quantitative analysis of F0 height in the 573 non-falling tokens w/sufficient data for analysis Examined question nucleus (nucF0) and tail (btF0) only Speaker-normalized (z-score) F0 of: 1. nuclear accent (nucF0) 2. rightmost edge of question (btF0) 3. difference between 1 & 2 (riserange)

Question Form and F0 DeclQs and YNQs both thought to rise (H*H- H% vs. L*H-H%?): Are there F0 height differences between them? 2-way ANOVA on form x function: FORM:nucF0: F(5)=19.34, p=0 btF0: F(5)10.71, p=0 riserange: F(5)=3.6, p<.01 Planned comparisons (Tukey, alpha=.01) show no difference between declarative Qs and yes-no Qs Main effect of form caused by yes-no tags (low F0) and particles (high F0)

Normalized means at nucF0 and btF0 chkclarinfochkclarinfo

Question Function and F0 Question dialog acts thought to correlate with F0: Does question FUNCTION affect F0? 2-way ANOVA on form x function: FUNCTION: nucF0: F(3)=16.6, p=0 btF0: F(3)=8.56, p<.001 riserange: F(3)=3.94, p<.01 Main effect; planned comparisons show: clarQ > chkQ (nucF0 & btF0) infoQ > clarQ/chkQ (nucF0) No interactions for any measure

Clarification types and F0 1Channel: Problem hearing if the tutor actually said something or not (Huh?, Hm?) 2Perception: Problem hearing what the tutor said (‘G’ as in God?, Did you say a word or a letter?, including reprise/echo questions (A what?) 3Understanding: Problem with reference resolution (This up here?, What did I imply or what does the statement imply?), or with general understanding (Is that the same thing or is that different?, What do you mean?) 4Intention: Problem determining what the tutor intended by his utterance (You want an exact number?, Uh are you asking me another characteristic of freefall?) +Non-interlocutor-related (NIR): Problem understanding the task (Am I supposed to speak this or type it?), or clarification of the examination question (Should I assume both vehicles are going at the same speed?) Clark ‘96 levels of coordination: sources of communication problems

Effects of Clarification Type One-way ANOVA combining levels 1&2 into single acoustic/perceptual category: nucF0: F(3)=5.41, p=.001 btF0: F(3)=6.6, p<.001 riserange: F(3)=2.59, p=.05 Main effect for clarification type Ranking for each measure: higher F0 > > > > > > > > > > > > > > > lower F0 acoust/percept > understanding > NIR > intention Planned comparisons (Tukey, alpha=.01) show only significant comparison was acoust/percep > intention

Can Prosody Distinguish Question Form? Question Function? Only a few question forms prosodically distinct in our study – lexico/syntactic information can help Question function more successfully differentiated prosodically – where there is less reliable lexico/syntactic information Can we use prosodic information with lexico- syntactic information to help identify question form and function automatically?

Detecting Student Questions Syntax Wh-words, subject/auxiliary inversion Prosody Phrase-final rising intonation (Pierrehumbert & Hirschberg ‘90) Duration and pausing (Shriberg et al. ‘98) Lexico-pragmatics personal pronouns, utterance-initial pronouns (Geluykens 1987; Beun 1990)

Corpus 141 ITSpoke dialogues 5 hours of student speech Student turns average 2.5 seconds 1,030 questions 25 questions per hour 70% of turns consist entirely of the question 89% of questions are turn-final

Question Form Distribution in ITSpoke FormExampleDistr. yes/noIs that right?24% wh-What do you mean?10% yes/no tagIt will stay the same, right?7% alternativeForce or something?3% particleHuh?2% declarativeThe weight?54%

Question-Bearing Turns Contain one or more questions N = 918

Features Extracted Prosodic pitch loudness pausing speaking rate calculated over entire turn and last 200 ms Syntactic unigram and bigram part-of-speech tags

Feature Extraction Lexical unigram and bigram hand-labeled transcriptions Student and task dependent pre-test score gender correctness previous tutor dialogue act

Machine Learning Experiments Question-bearing vs. non-question-bearing Down-sampled to 50/50 distribution Experimented by feature type Adaboosted C4.5 decision trees 5-fold cross validation Best results with all features Accuracy = 79.7% Precision = Recall = F-measure = 0.8

Accuracy by Feature Type prosody: pausing and speaking rate52.6% student and task dependent56.1% prosody: loudness61.8% syntactic65.3% lexical67.2% prosody: last 200 ms70.3% prosody: pitch72.6% prosody: all74.5%

Feature Type Discussion Which features most informative? pitch slope of last 200 ms and entire turn maximum and mean pitch of turn Which features most often used in learning? pre-test score slope of last 200 ms maximum pitch of entire turn cumulative pause duration

Other Observations Syntactic features were informative personal pronoun + verb, wh-pronoun, interjection Lexical features were informative yes, right, what, I, you

Conclusions Most questions in our tutoring corpus are declarative in form More than syntax is needed to identify these as questions Prosodic features are very important Detecting question-bearing turns is possible Detecting question function is needed

Question Forms in ITSpoke FormDistr.Example declarative54%The weight? yes/no24%Is that right? wh-10%What do you mean? yes/no tag7%It will stay the same, right? alternative3%Force or something? particle2%Huh?