Heather Pon-Barry


Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue
Ranjini Das | Heather Pon-Barry
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

In this paper, we apply the contribution model of grounding to a corpus of human-human peer-mentoring dialogues. From this analysis, we propose effective turn-taking strategies for human-robot interaction with a teachable robot. Specifically, we focus on (1) how robots can encourage humans to present and (2) how robots can signal that they are going to begin a new presentation. We evaluate the strategies against a corpus of human-robot dialogues and offer three guidelines for teachable robots to follow to achieve more human-like collaborative dialogue.


Finding Eyewitness Tweets During Crises
Fred Morstatter | Nichola Lubold | Heather Pon-Barry | Jürgen Pfeffer | Huan Liu
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

Discourse Analysis of User Forums in an Online Weight Loss Application
Lydia Manikonda | Heather Pon-Barry | Subbarao Kambhampati | Eric Hekler | David W. McDonald
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

Eliciting and Annotating Uncertainty in Spoken Language
Heather Pon-Barry | Stuart Shieber | Nicholas Longenbaugh
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

A major challenge in the field of automatic recognition of emotion and affect in speech is the subjective nature of affect labels. The most common approach to acquiring affect labels is to ask a panel of listeners to rate a corpus of spoken utterances along one or more dimensions of interest. For applications ranging from educational technology to voice search to dictation, a speaker’s level of certainty is a primary dimension of interest. In such applications, we would like to know the speaker’s actual level of certainty, but past research has only revealed listeners’ perception of the speaker’s level of certainty. In this paper, we present a method for eliciting spoken utterances using stimuli that we design such that they have a quantitative, crowdsourced legibility score. While we cannot control a speaker’s actual internal level of certainty, the use of these stimuli provides a better estimate of internal certainty compared to existing speech corpora. The Harvard Uncertainty Speech Corpus, containing speech data, certainty annotations, and prosodic features, is made available to the research community.


The Importance of Sub-Utterance Prosody in Predicting Level of Certainty
Heather Pon-Barry | Stuart Shieber
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers


Interactive Question Answering and Constraint Relaxation in Spoken Dialogue Systems
Sebastian Varges | Fuliang Weng | Heather Pon-Barry
Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue


A Flexible Conversational Dialog System for MP3 Player
Fuliang Weng | Lawrence Cavedon | Badri Raghunathan | Danilo Mirkovic | Ben Bei | Heather Pon-Barry | Harry Bratt | Hua Cheng | Hauke Schmidt | Rohit Mishra | Brian Lathrop | Qi Zhang | Tobias Scheideck | Kui Xu | Tess Hand-Bender | Stanley Peters | Liz Shriberg | Carsten Bergmann
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations


Automated Tutoring Dialogues for Training in Shipboard Damage Control
John Fry | Matt Ginzton | Stanley Peters | Brady Clark | Heather Pon-Barry
Proceedings of the Second SIGdial Workshop on Discourse and Dialogue