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Classification of Discourse Functions of Affirmative Words in Spoken Dialogue

INTERSPEECH, Antwerp, August 2007. Classification of Discourse Functions of Affirmative Words in Spoken Dialogue. Agust ín Gravano, Stefan Benus, Julia Hirschberg Shira Mitchell, Ilia Vovsha. Spoken Language Processing Group Columbia University. Cue Words.

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Classification of Discourse Functions of Affirmative Words in Spoken Dialogue

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  1. INTERSPEECH, Antwerp, August 2007 Classification of Discourse Functions of Affirmative Words in Spoken Dialogue Agustín Gravano, Stefan Benus, Julia Hirschberg Shira Mitchell, Ilia Vovsha Spoken Language Processing Group Columbia University

  2. Cue Words • Ambiguous linguistic expressions used for • Making a semantic contribution, or • Conveying a pragmatic function. Examples: now, well, so, alright, and, okay, first, by the way, on the other hand. • Single affirmative cue words • Examples: alright, okay, mm-hm, right, uh-huh, yes. • May be used to convey acknowledgment or agreement, to change topic, to backchannel, etc. Agustín Gravano INTERSPEECH 2007

  3. Research Goals • Learn which features best characterize the different functions of single affirmative cue words. • Determine how these can be identified automatically. • Important in Spoken Dialogue Systems: • Understand user input. • Produce output appropriately. Agustín Gravano INTERSPEECH 2007

  4. Previous Work • Classification of cue words into discourse vs. sentential use. • Hirschberg & Litman ’87, ’93; Litman ’94; Heeman, Byron & Allen ’98; Zufferey & Popescu-Belis ’04. • In our corpus: • right: 15% discourse, 85% sentential. • All other affirmative cue words: 99% disc., 1% sent. • Discourse vs. sentential distinction insufficient. Need to define new classification tasks. Agustín Gravano INTERSPEECH 2007

  5. Talk Overview • Columbia Games Corpus • Classification tasks • Experimental features • Results Agustín Gravano INTERSPEECH 2007

  6. The Columbia Games Corpus • 12 spontaneous task-oriented dyadic conversations in Standard American English. • 2 subjects playing computer games; no eye contact. Agustín Gravano INTERSPEECH 2007

  7. The Columbia Games CorpusFunction of Affirmative 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: Ack/Agree + Cue begin • Pivot ending: Ack/Agree + Cue end 7.9% of the words in our corpus Agustín Gravano INTERSPEECH 2007

  8. The Columbia Games CorpusFunction of Affirmative Cue Words • Literal Modifier that’s pretty much okay • Backchannel Speaker 1: between the yellow mermaid and the whaleSpeaker 2:okaySpeaker 1: and it is • Cue beginning discourse segment okay we gonna be placing the blue moon Agustín Gravano INTERSPEECH 2007

  9. The Columbia Games CorpusFunction of Affirmative Cue Words • 3 trained labelers • Inter-labeler agreement: • Fleiss’ Kappa = 0.69 (Fleiss ’71) • In this study we use the majority label for each affirmative cue word. • Majority label: label chosen by at least two of the three labelers. Agustín Gravano INTERSPEECH 2007

  10. MethodTwo new classification tasks • Identification of a discourse segment boundary function • Segment beginning vs. Segment end vs. No discourse segment boundary function • Identification of an acknowledgment function • Acknowledgment vs. No acknowledgment Agustín Gravano INTERSPEECH 2007

  11. MethodMachine Learning Experiments • ML Algorithm • JRip: Weka’s implementation of the propositional rule learner Ripper (Cohen ’95). • We also tried J4.8, Weka’s implementation of the decision tree learner C4.5 (Quinlan ’93, ’96), with similar results. • 10-fold cross validation in all experiments. Agustín Gravano INTERSPEECH 2007

  12. MethodExperimental features • IPU (Inter-pausal unit) • Maximal sequence of words delimited by pause > 50ms. • Conversational Turn • Maximal sequence of IPUs by the same speaker, with no contribution from the other speaker. Agustín Gravano INTERSPEECH 2007

  13. MethodExperimental features • Text-based features • Extracted from the text transcriptions. • Lexical id; POS tags; position of word in IPU / turn; etc. • Timing features • Extracted from the time alignment of the transcriptions. • Word / IPU / turn duration; amount of overlap; etc. • Acoustic features • {min, mean, max, stdev} x {pitch, intensity} • Slope of pitch, stylized pitch, and intensity, over the whole word, and over its last 100, 200, 300ms. • Acoustic features from the end of the other speaker’s previous turn. Agustín Gravano INTERSPEECH 2007

  14. ResultsDiscourse segment boundary function (1) Majority class baseline: NO BOUNDARY. (2) Calculated wrt each labeler’s agreement with the majority labels. Agustín Gravano INTERSPEECH 2007

  15. ResultsAcknowledgment function (1) Baseline based on lexical identity: {huh, right } no ACK all other words ACK (2) Calculated wrt each labeler’s agreement with the majority labels. Agustín Gravano INTERSPEECH 2007

  16. Best-performing features Agustín Gravano INTERSPEECH 2007

  17. ResultsClassification of individual words • Classification of each individual word into its most common functions. • alright Ack/Agree, Cue Begin, Other • mm-hm  Ack/Agree, Backchannel • okay  Ack/Agree, Backchannel, Cue Begin, Ack+CueBegin, Ack+CueEnd, Other • right  Ack/Agree, Check, Literal Modifier • yeah  Ack/Agree, Backchannel Agustín Gravano INTERSPEECH 2007

  18. ResultsClassification of the word ‘okay’ (1) Majority class baseline: ACK/AGREE. (2) Calculated wrt each labeler’s agreement with the majority labels. Agustín Gravano INTERSPEECH 2007

  19. Summary • Discourse/sentential distinction is insufficient for affirmative cue words in spoken dialogue. • Two new classification tasks: • Detection of an acknowledgment function. • Detection of a discourse boundary function. • Best performing ML models: • Based on textual and timing features. • Slight improvement when using acoustic features. Agustín Gravano INTERSPEECH 2007

  20. Further Work • Gravano et al, 2007 On the role of context and prosody in the interpretation of ‘okay’.ACL 2007, Prague, Czech Republic, June 2007. • Benus et al, 2007 The prosody of backchannels in American English. ICPhS 2007, Saarbrücken, Germany, August 2007. Agustín Gravano INTERSPEECH 2007

  21. INTERSPEECH, Antwerp, August 2007 Classification of Discourse Functions of Affirmative Words in Spoken Dialogue Agustín Gravano, Stefan Benus, Julia Hirschberg Shira Mitchell, Ilia Vovsha Spoken Language Processing Group Columbia University

  22. Agustín Gravano INTERSPEECH 2007

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