High Frequency Word Entrainment in Spoken Dialogue

Slides:



Advertisements
Similar presentations
Agustín Gravano 1,2 Julia Hirschberg 1 (1)Columbia University, New York, USA (2) Universidad de Buenos Aires, Argentina Backchannel-Inviting Cues in Task-Oriented.
Advertisements

“Effect of Genre, Speaker, and Word Class on the Realization of Given and New Information” Julia Agustín Gravano & Julia Hirschberg {agus,
“Downstepped contours in the given/new distinction” Agustín Gravano Spoken Language Processing Group Columbia University, New York On the Role of Prosody.
5/10/20151 Evaluating Spoken Dialogue Systems Julia Hirschberg CS 4706.
Agustín Gravano 1 · Stefan Benus 2 · Julia Hirschberg 1 Elisa Sneed German 3 · Gregory Ward 3 1 Columbia University 2 Univerzity Konštantína Filozofa.
SIGDIAL 2007, Antwerpen1 Measuring Adaptation Between Dialogs Svetlana Stoyancheva Amanda Stent SUNY, Stony Brook.
Extracting Social Meaning Identifying Interactional Style in Spoken Conversation Jurafsky et al ‘09 Presented by Laura Willson.
High Frequency Word Entrainment in Spoken Dialogue ACL, June Columbus, OH Department of Computer and Information Science University of Pennsylvania.
Context and Prosody in the Interpretation of Cue Phrases in Dialogue Julia Hirschberg Columbia University and KTH 11/22/07 Spoken Dialog with Humans and.
Turn-taking in Mandarin Dialogue: Interactions of Tone and Intonation Gina-Anne Levow University of Chicago October 14, 2005.
Classification of Discourse Functions of Affirmative Words in Spoken Dialogue Julia Agustín Gravano, Stefan Benus, Julia Hirschberg Shira Mitchell, Ilia.
Agustín Gravano 1,2 Julia Hirschberg 1 (1)Columbia University, New York, USA (2) Universidad de Buenos Aires, Argentina Turn-Yielding Cues in Task-Oriented.
PSY 369: Psycholinguistics Language Production & Comprehension: Conversation & Dialog.
Interactive Dialogue Systems Professor Diane Litman Computer Science Department & Learning Research and Development Center University of Pittsburgh Pittsburgh,
A Study in Cross-Cultural Interpretations of Back-Channeling Behavior Yaffa Al Bayyari Nigel Ward The University of Texas at El Paso Department of Computer.
Academia Británica Pulling teeth UTTERANCE above ALL March ̍11 UTTERANCE above ALL Academia Británica Pulling teeth March ̍11 um, so...what are we talkin’about?
PSY 369: Psycholinguistics Conversation & Dialog: Language Production and Comprehension in conjoined action.
Collaborative Research: Monitoring Student State in Tutorial Spoken Dialogue Diane Litman Computer Science Department and Learning Research and Development.
Predicting Student Emotions in Computer-Human Tutoring Dialogues Diane J. Litman&Kate Forbes-Riley University of Pittsburgh Department of Computer Science.
The Games Corpus Design, implementation and annotation Agustín Gravano Spoken Language Processing Group Columbia University.
Modeling Latent Biographic Attributes in Conversational Genres Nikesh Garera David Yarowsky.
Event-Based Extractive Summarization E. Filatova and V. Hatzivassiloglou Department of Computer Science Columbia University (ACL 2004)
Lexical, Prosodic, and Syntactics Cues for Dialog Acts.
Modeling Student Benefits from Illustrations and Graphs Michael Lipschultz Diane Litman Computer Science Department University of Pittsburgh.
Acoustic Cues to Emotional Speech Julia Hirschberg (joint work with Jennifer Venditti and Jackson Liscombe) Columbia University 26 June 2003.
On the role of context and prosody in the interpretation of ‘okay’ Julia Agustín Gravano, Stefan Benus, Julia Hirschberg Héctor Chávez, and Lauren Wilcox.
Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System Diane J. Litman AT&T Labs -- Research
Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources Kate Forbes-Riley and Diane Litman Learning Research and Development Center and Computer.
HOW TO WRITE A TRANSCRIPT
Ma Rui Tianjin Normal University
Grounding by nodding GESPIN 2009, Poznan, Poland
Towards Emotion Prediction in Spoken Tutoring Dialogues
Unit 1 Greeting and Introducing People
张昊.
Recognizing Structure: Dialogue Acts and Segmentation
Studying Intonation Julia Hirschberg CS /21/2018.
Issues in Spoken Dialogue Systems
Spoken Dialogue Systems
The interactive alignment model
Agustín Gravano1,2 Julia Hirschberg1
Dialogue Acts Julia Hirschberg CS /18/2018.
Comparing American and Palestinian Perceptions of Charisma Using Acoustic-Prosodic and Lexical Analysis Fadi Biadsy, Julia Hirschberg, Andrew Rosenberg,
Entrainment in SDS Julia Hirschberg CS /18/2018.
Turn-taking and Disfluencies
Julia Hirschberg Columbia University SIGdial 2008
Recognizing Structure: Sentence, Speaker, andTopic Segmentation
LEARNING ABOUT CONVERSATION
Searching and Summarizing Speech
“Downstepped contours in the given/new distinction”
Turn-taking and Disfluencies
Dialogue Acts Julia Hirschberg LSA /29/2018.
Fadi Biadsy. , Andrew Rosenberg. , Rolf Carlson†, Julia Hirschberg
Searching and Summarizing Speech
Implications of interactive alignment
Agustín Gravano & Julia Hirschberg {agus,
Spoken Dialogue Systems
Discourse Structure in Generation
Agustín Gravano1,2 Julia Hirschberg1
Tetsuya Nasukawa, IBM Tokyo Research Lab
Agustín Gravano1 · Stefan Benus2 · Julia Hirschberg1
Learning a Policy for Opportunistic Active Learning
English Language Norms: Interacting in Meaningful Ways
FCE (FIRST CERTIFICATE IN ENGLISH) General information.
Recognizing Structure: Dialogue Acts and Segmentation
Machine Learning in Practice Lecture 6
HOW TO TEACH SPEAKING Joko Nurkamto UNS Solo.
Learning about Listening
Low Level Cues to Emotion
Acoustic-Prosodic and Lexical Entrainment in Deceptive Dialogue
Guest Lecture: Advanced Topics in Spoken Language Processing
Presentation transcript:

High Frequency Word Entrainment in Spoken Dialogue ACL, June 2008 - Columbus, OH High Frequency Word Entrainment in Spoken Dialogue Ani Nenkova - Agustín Gravano - Julia Hirschberg Department of Computer and Information Science University of Pennsylvania - Philadelphia, PA Department of Computer Science Columbia University - New York, NY

Agustín Gravano - ACL - June 2008 Entrainment In conversation, people adapt the way they speak to match their partners’. Entrainment, accommodation, adaptation, alignment, convergence. Agustín Gravano - ACL - June 2008

Previous Work Existence of entrainment In conversation, speakers: Negotiate common ways of describing things. S.E. Brennan, 1996 Alter their intensity to match their partners’. R. Coulston et al., 2002 A. Ward & D. Litman, 2007 Reuse syntactic constructions. D. Reitter et al., 2006 Agustín Gravano - ACL - June 2008

Previous Work Role of entrainment Entrainment at different levels (lex, syn, sem): Is key for both production and understanding, and facilitates interaction. M.J. Pickering & S. Garrod, 2004 D. Goleman, 2006 Is a good predictor of task success (MapTask). D. Reitter & J. Moore, 2007 Agustín Gravano - ACL - June 2008

Agustín Gravano - ACL - June 2008 This Work Novel measures of entrainment based on usage of high-frequency words (HFW). Entrainment and… Perceived naturalness Task success Dialogue coordination Implications in the development of Spoken Dialogue Systems. Agustín Gravano - ACL - June 2008

Agustín Gravano - ACL - June 2008 High-Frequency Words Most common words in a corpus, or in a conversation. Typically, function words and cue words. Entrainment of HFW Domain-independent Agustín Gravano - ACL - June 2008

Entrainment & Naturalness Will a conversation be perceived as more natural if HFW entrainment occurs? Switchboard corpus 2430 spontaneous telephone conversations in American English Speakers asked to discuss a pre-assigned topic Annotated for degree of perceived naturalness, from “1” (Very natural) to “5” (Not natural at all). Agustín Gravano - ACL - June 2008

Entrainment & Naturalness Measure of Entrainment Where fraction(w, Si)  Fraction of times Speaker i used word w in the conversation Examples entr(‘okay’)   | 10 / 500 – 8 / 600 |   0.0067 entr(‘yeah’)   | 1 / 500 – 30 / 600 |   0.048 Agustín Gravano - ACL - June 2008

Entrainment & Naturalness Machine Learning Task Predict the perceived naturalness of conversations. Binary decision, over balanced data 250 conversations rated “1” (very natural), and 250 with ratings “3”, “4” or “5”. Computed entr(w) for the 100 most frequent words in the entire Switchboard corpus. Feature selection: 25 most predictive words. um, how, okay, go, I’ve, all, very, as, or, up, a, no, more, something, from, this, what, too, got, can, he, in, things, you, and. Agustín Gravano - ACL - June 2008

Entrainment & Naturalness Results Logistic regression model (10-fold CV): 63.76% accuracy (significantly better than 50% baseline) Entrainment in usage of HFW is a good indicator of perceived naturalness. Agustín Gravano - ACL - June 2008

Entrainment & Task Success Is a conversation more likely to succeed when HFW entrainment occurs? Columbia Games Corpus 12 spontaneous task-oriented dialogues in American English, with no eye contact. Each pair of subjects played a series of computer-based matching games. Subjects received a score after each task. Agustín Gravano - ACL - June 2008

Entrainment & Task Success Measures of Entrainment Where c = Class of words countSi(w) = No. of times Si used word w in the conversation Agustín Gravano - ACL - June 2008

Entrainment & Task Success Word Classes 25MF-G: 25 most frequent words in the game 25MF-C: 25 most frequent words in the corpus the, a, okay, and, of, I, on, right, is, it, that, have,… ACW: Affirmative cue words alright, mm-hm, okay, right, uh-huh, yeah, yes 7.9% of all words in the Games Corpus Agustín Gravano - ACL - June 2008

Entrainment & Task Success Results Correlations with game score: HFW entrainment positively correlated with task success. Word class ENTR1 cor (p) ENTR2 cor (p) 25MF-C 0.341 (0.02) 0.187 (0.20) 25MF-G 0.376 (0.01) 0.260 (0.07) ACW 0.230 (0.12) 0.372 (0.01) Agustín Gravano - ACL - June 2008

Entrainment & Coordination Is dialogue more coordinated when HFW entrainment occurs? Columbia Games Corpus Labeled for type of turn exchanges (Beattie, 1982), including: Smooth Switch: S2 starts his turn after S1 has finished hers Interruption: S2 starts his turn before S1 has finished hers Overlap: S2 starts his turn just before S1 has finished hers, but without interrupting. Agustín Gravano - ACL - June 2008

Entrainment & Coordination Results Significant correlations (p<0.05): ENTR1(ACW) & Prop. of Overlaps (cor = 0.64) ENTR2(ACW) & Prop. of Overlaps (cor = 0.61) ENTR2(25MF-G) & Prop. of Overlaps (cor = 0.60) ENTR1(25MF-C) & Prop. of Interruptions (cor = – 0.61) ENTR2(ACW) & Mean Latency of Smooth Switches (cor = – 0.76) HFW entrainment positively correlated with more overlaps, fewer interruptions, and shorter inter-turn latencies. Agustín Gravano - ACL - June 2008

Agustín Gravano - ACL - June 2008 Conclusion Two novel measures of lexical entrainment, based on the usage of high-frequency words. Entrainment in usage of high-frequency words is correlated with: Perceived naturalness Task success Dialogue coordination Implications in the development of SDS. Agustín Gravano - ACL - June 2008

High Frequency Word Entrainment in Spoken Dialogue ACL, June 2008 - Columbus, OH High Frequency Word Entrainment in Spoken Dialogue Ani Nenkova - Agustín Gravano - Julia Hirschberg Department of Computer and Information Science University of Pennsylvania - Philadelphia, PA Department of Computer Science Columbia University - New York, NY