Julia Hirschberg

Also published as: Julia B. Hirschberg


2021

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“Talk to me with left, right, and angles”: Lexical entrainment in spoken Hebrew dialogue
Andreas Weise | Vered Silber-Varod | Anat Lerner | Julia Hirschberg | Rivka Levitan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

It has been well-documented for several languages that human interlocutors tend to adapt their linguistic productions to become more similar to each other. This behavior, known as entrainment, affects lexical choice as well, both with regard to specific words, such as referring expressions, and overall style. We offer what we believe to be the first investigation of such lexical entrainment in Hebrew. Using two existing measures, we analyze Hebrew speakers interacting in a Map Task, a popular experimental setup, and find rich evidence of lexical entrainment. Analyzing speaker pairs by the combination of their genders as well as speakers by their individual gender, we find no clear pattern of differences. We do, however, find that speakers in a position of less power entrain more than those with greater power, which matches theoretical accounts. Overall, our results mostly accord with those for American English, with a lack of entrainment on hedge words being the main difference.

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Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts
Zhengping Jiang | Jonathan Zomick | Sarah Ita Levitan | Mark Serper | Julia Hirschberg
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

We address the problem of predicting psychiatric hospitalizations using linguistic features drawn from social media posts. We formulate this novel task and develop an approach to automatically extract time spans of self-reported psychiatric hospitalizations. Using this dataset, we build predictive models of psychiatric hospitalization, comparing feature sets, user vs. post classification, and comparing model performance using a varying time window of posts. Our best model achieves an F1 of .718 using 7 days of posts. Our results suggest that this is a useful framework for collecting hospitalization data, and that social media data can be leveraged to predict acute psychiatric crises before they occur, potentially saving lives and improving outcomes for individuals with mental illness.

2020

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Detection of Mental Health from Reddit via Deep Contextualized Representations
Zhengping Jiang | Sarah Ita Levitan | Jonathan Zomick | Julia Hirschberg
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.

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Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies
Xi (Leslie) Chen | Sarah Ita Levitan | Michelle Levine | Marko Mandic | Julia Hirschberg
Transactions of the Association for Computational Linguistics, Volume 8

Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality. With this data we built classifiers to automatically distinguish trusted from mistrusted speech, achieving an F1 of 66.1%. We next evaluated whether the strategies raters said they used to discriminate between truthful and deceptive responses were in fact useful. Our results show that, although several prosodic and lexical features were consistently perceived as trustworthy, they were not reliable cues. Also, the strategies that judges reported using in deception detection were not helpful for the task. Our work sheds light on the nature of trusted language and provides insight into the challenging problem of human deception detection.

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A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter
Ishaan Arora | Julia Guo | Sarah Ita Levitan | Susan McGregor | Julia Hirschberg
Proceedings of the Fourth Workshop on Online Abuse and Harms

Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators – not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by non-targets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists’ experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.

2019

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SpatialNet: A Declarative Resource for Spatial Relations
Morgan Ulinski | Bob Coyne | Julia Hirschberg
Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)

This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.

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Crowdsourced Hedge Term Disambiguation
Morgan Ulinski | Julia Hirschberg
Proceedings of the 13th Linguistic Annotation Workshop

We address the issue of acquiring quality annotations of hedging words and phrases, linguistic phenomenona in which words, sounds, or other constructions are used to express ambiguity or uncertainty. Due to the limited availability of existing corpora annotated for hedging, linguists and other language scientists have been constrained as to the extent they can study this phenomenon. In this paper, we introduce a new method of acquiring hedging annotations via crowdsourcing, based on reformulating the task of labeling hedges as a simple word sense disambiguation task. We also introduce a new hedging corpus we have constructed by applying this method, a collection of forum posts annotated using Amazon Mechanical Turk. We found that the crowdsourced judgments we obtained had an inter-annotator agreement of 92.89% (Fleiss’ Kappa=0.751) and, when comparing a subset of these annotations to an expert-annotated gold standard, an accuracy of 96.65%.

2018

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Linguistic Cues to Deception and Perceived Deception in Interview Dialogues
Sarah Ita Levitan | Angel Maredia | Julia Hirschberg
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We explore deception detection in interview dialogues. We analyze a set of linguistic features in both truthful and deceptive responses to interview questions. We also study the perception of deception, identifying characteristics of statements that are perceived as truthful or deceptive by interviewers. Our analysis show significant differences between truthful and deceptive question responses, as well as variations in deception patterns across gender and native language. This analysis motivated our selection of features for machine learning experiments aimed at classifying globally deceptive speech. Our best classification performance is 72.74% F1-Score (about 17% better than human performance), which is achieved using a combination of linguistic features and individual traits.

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Collecting Code-Switched Data from Social Media
Gideon Mendels | Victor Soto | Aaron Jaech | Julia Hirschberg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Evaluating the WordsEye Text-to-Scene System: Imaginative and Realistic Sentences
Morgan Ulinski | Bob Coyne | Julia Hirschberg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Using Hedge Detection to Improve Committed Belief Tagging
Morgan Ulinski | Seth Benjamin | Julia Hirschberg
Proceedings of the Workshop on Computational Semantics beyond Events and Roles

We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.

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Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
Gustavo Aguilar | Fahad AlGhamdi | Victor Soto | Thamar Solorio | Mona Diab | Julia Hirschberg
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

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Joint Part-of-Speech and Language ID Tagging for Code-Switched Data
Victor Soto | Julia Hirschberg
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34% accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.

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Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
Gustavo Aguilar | Fahad AlGhamdi | Victor Soto | Mona Diab | Julia Hirschberg | Thamar Solorio
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.

2017

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Comparing Approaches for Automatic Question Identification
Angel Maredia | Kara Schechtman | Sarah Ita Levitan | Julia Hirschberg
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia. Typically, these corpora are labeled by human annotators, either in the lab or through crowd-sourcing; however, this is cumbersome and time-consuming for large corpora. We present four different approaches to automatically tagging a corpus when general topics of the conversations are known. We develop these approaches on the Columbia X-Cultural Deception corpus and find accuracy that significantly exceeds the baseline. Finally, we conduct a cross-corpus evaluation by testing the best performing approach on the Columbia/SRI/Colorado corpus.

2016

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Identifying Individual Differences in Gender, Ethnicity, and Personality from Dialogue for Deception Detection
Sarah Ita Levitan | Yocheved Levitan | Guozhen An | Michelle Levine | Rivka Levitan | Andrew Rosenberg | Julia Hirschberg
Proceedings of the Second Workshop on Computational Approaches to Deception Detection

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Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search
Gideon Mendels | Erica Cooper | Julia Hirschberg
Proceedings of the 10th Web as Corpus Workshop

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Proceedings of the Second Workshop on Computational Approaches to Code Switching
Mona Diab | Pascale Fung | Mahmoud Ghoneim | Julia Hirschberg | Thamar Solorio
Proceedings of the Second Workshop on Computational Approaches to Code Switching

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Part of Speech Tagging for Code Switched Data
Fahad AlGhamdi | Giovanni Molina | Mona Diab | Thamar Solorio | Abdelati Hawwari | Victor Soto | Julia Hirschberg
Proceedings of the Second Workshop on Computational Approaches to Code Switching

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Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics
Morgan Ulinski | Julia Hirschberg | Owen Rambow
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present experiments in incrementally learning a dependency parser. The parser will be used in the WordsEye Linguistics Tools (WELT) (Ulinski et al., 2014) which supports field linguists documenting a language’s syntax and semantics. Our goal is to make syntactic annotation faster for field linguists. We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants. We collected descriptions for each picture and video from native speakers in English, Spanish, German, and Egyptian Arabic. We compare the performance of MSTParser (McDonald et al., 2006) and MaltParser (Nivre et al., 2006) when trained on small amounts of this data. We find that MaltParser achieves the best performance. We also present the results of experiments using the parser to assist with annotation. We find that even when the parser is trained on a single sentence from the corpus, annotation time significantly decreases.

2015

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Acoustic-prosodic entrainment in Slovak, Spanish, English and Chinese: A cross-linguistic comparison
Rivka Levitan | Štefan Beňuš | Agustín Gravano | Julia Hirschberg
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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A New Dataset and Evaluation for Belief/Factuality
Vinodkumar Prabhakaran | Tomas By | Julia Hirschberg | Owen Rambow | Samira Shaikh | Tomek Strzalkowski | Jennifer Tracey | Michael Arrigo | Rupayan Basu | Micah Clark | Adam Dalton | Mona Diab | Louise Guthrie | Anna Prokofieva | Stephanie Strassel | Gregory Werner | Yorick Wilks | Janyce Wiebe
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2014

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Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages
Jeff Good | Julia Hirschberg | Owen Rambow
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Documenting Endangered Languages with the WordsEye Linguistics Tool
Morgan Ulinski | Anusha Balakrishnan | Daniel Bauer | Bob Coyne | Julia Hirschberg | Owen Rambow
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Proceedings of the First Workshop on Computational Approaches to Code Switching
Mona Diab | Julia Hirschberg | Pascale Fung | Thamar Solorio
Proceedings of the First Workshop on Computational Approaches to Code Switching

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Overview for the First Shared Task on Language Identification in Code-Switched Data
Thamar Solorio | Elizabeth Blair | Suraj Maharjan | Steven Bethard | Mona Diab | Mahmoud Ghoneim | Abdelati Hawwari | Fahad AlGhamdi | Julia Hirschberg | Alison Chang | Pascale Fung
Proceedings of the First Workshop on Computational Approaches to Code Switching

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Detecting Inappropriate Clarification Requests in Spoken Dialogue Systems
Alex Liu | Rose Sloan | Mei-Vern Then | Svetlana Stoyanchev | Julia Hirschberg | Elizabeth Shriberg
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Towards simultaneous interpreting: the timing of incremental machine translation and speech synthesis
Timo Baumann | Srinivas Bangalore | Julia Hirschberg
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

In simultaneous interpreting, human experts incrementally construct and extend partial hypotheses about the source speaker’s message, and start to verbalize a corresponding message in the target language, based on a partial translation – which may have to be corrected occasionally. They commence the target utterance in the hope that they will be able to finish understanding the source speaker’s message and determine its translation in time for the unfolding delivery. Of course, both incremental understanding and translation by humans can be garden-pathed, although experts are able to optimize their delivery so as to balance the goals of minimal latency, translation quality and high speech fluency with few corrections. We investigate the temporal properties of both translation input and output to evaluate the tradeoff between low latency and translation quality. In addition, we estimate the improvements that can be gained with a tempo-elastic speech synthesizer.

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Teenage and adult speech in school context: building and processing a corpus of European Portuguese
Ana Isabel Mata | Helena Moniz | Fernando Batista | Julia Hirschberg
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a corpus of European Portuguese spoken by teenagers and adults in school context, CPE-FACES, with an overview of the differential characteristics of high school oral presentations and the challenges this data poses to automatic speech processing. The CPE-FACES corpus has been created with two main goals: to provide a resource for the study of prosodic patterns in both spontaneous and prepared unscripted speech, and to capture inter-speaker and speaking style variations common at school, for research on oral presentations. Research on speaking styles is still largely based on adult speech. References to teenagers are sparse and cross-analyses of speech types comparing teenagers and adults are rare. We expect CPE-FACES, currently a unique resource in this domain, will contribute to filling this gap in European Portuguese. Focusing on disfluencies and phrase-final phonetic-phonological processes we show the impact of teenage speech on the automatic segmentation of oral presentations. Analyzing fluent final intonation contours in declarative utterances, we also show that communicative situation specificities, speaker status and cross-gender differences are key factors in speaking style variation at school.

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WELT: Using Graphics Generation in Linguistic Fieldwork
Morgan Ulinski | Anusha Balakrishnan | Bob Coyne | Julia Hirschberg | Owen Rambow
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2013

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Exploring Features For Localized Detection of Speech Recognition Errors
Eli Pincus | Svetlana Stoyanchev | Julia Hirschberg
Proceedings of the SIGDIAL 2013 Conference

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Modelling Human Clarification Strategies
Svetlana Stoyanchev | Alex Liu | Julia Hirschberg
Proceedings of the SIGDIAL 2013 Conference

2012

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Detecting Hate Speech on the World Wide Web
William Warner | Julia Hirschberg
Proceedings of the Second Workshop on Language in Social Media

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Affirmative Cue Words in Task-Oriented Dialogue
Agustín Gravano | Julia Hirschberg | Štefan Beňuš
Computational Linguistics, Volume 38, Issue 1 - March 2012

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Acoustic-Prosodic Entrainment and Social Behavior
Rivka Levitan | Agustín Gravano | Laura Willson | S̆tefan Ben̆us̆ | Julia Hirschberg | Ani Nenkova
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages
Ani Nenkova | Julia Hirschberg | Yang Liu
Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages

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Detecting Levels of Interest from Spoken Dialog with Multistream Prediction Feedback and Similarity Based Hierarchical Fusion Learning
William Yang Wang | Julia Hirschberg
Proceedings of the SIGDIAL 2011 Conference

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Entrainment in Speech Preceding Backchannels.
Rivka Levitan | Agustín Gravano | Julia Hirschberg
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2009

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Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-Based Pronunciation Rules
Fadi Biadsy | Nizar Habash | Julia Hirschberg
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Detecting Pitch Accents at the Word, Syllable and Vowel Level
Andrew Rosenberg | Julia Hirschberg
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Spoken Arabic Dialect Identification Using Phonotactic Modeling
Fadi Biadsy | Julia Hirschberg | Nizar Habash
Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages

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Turn-Yielding Cues in Task-Oriented Dialogue
Agustín Gravano | Julia Hirschberg
Proceedings of the SIGDIAL 2009 Conference

2008

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Speaking More Like You: Lexical, Acoustic/Prosodic, and Discourse Entrainment in Spoken Dialogue Systems
Julia Hirschberg
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

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An Unsupervised Approach to Biography Production Using Wikipedia
Fadi Biadsy | Julia Hirschberg | Elena Filatova
Proceedings of ACL-08: HLT

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High Frequency Word Entrainment in Spoken Dialogue
Ani Nenkova | Agustín Gravano | Julia Hirschberg
Proceedings of ACL-08: HLT, Short Papers

2007

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V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure
Andrew Rosenberg | Julia Hirschberg
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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On the role of context and prosody in the interpretation of ‘okay’
Agustín Gravano | Stefan Benus | Héctor Chávez | Julia Hirschberg | Lauren Wilcox
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Characterizing and Predicting Corrections in Spoken Dialogue Systems
Diane Litman | Marc Swerts | Julia Hirschberg
Computational Linguistics, Volume 32, Number 3, September 2006

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Summarizing Speech Without Text Using Hidden Markov Models
Sameer Maskey | Julia Hirschberg
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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Story Segmentation of Broadcast News in English, Mandarin and Arabic
Andrew Rosenberg | Julia Hirschberg
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

2004

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Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies
Michel Galley | Kathleen McKeown | Julia Hirschberg | Elizabeth Shriberg
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2001

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Identifying User Corrections Automatically in Spoken Dialogue Systems
Julia Hirschberg | Diane Litman | Marc Swerts
Second Meeting of the North American Chapter of the Association for Computational Linguistics

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Predicting User Reactions to System Error
Diane Litman | Julia Hirschberg | Marc Swerts
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

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Labeling Corrections and Aware Sites in Spoken Dialogue Systems
Julia Hirschberg | Marc Swerts | Diane Litman
Proceedings of the Second SIGdial Workshop on Discourse and Dialogue

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SCANMail: Audio Navigation in the Voicemail Domain
Michiel Bacchiani | Julia Hirschberg | Aaron Rosenberg | Steve Whittaker | Donald Hindle | Phil Isenhour | Mark Jones | Litza Stark | Gary Zamchick
Proceedings of the First International Conference on Human Language Technology Research

2000

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Modeling Local Context for Pitch Accent Prediction
Shimei Pan | Julia Hirschberg
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

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Predicting Automatic Speech Recognition Performance Using Prosodic Cues
Diane J. Litman | Julia B. Hirschberg | Marc Swerts
1st Meeting of the North American Chapter of the Association for Computational Linguistics

1998

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I just played that a minute ago!:” Designing User Interfaces for Audio Navigation
Julia Hirschberg | John Choi | Christine Nakatani | Steve Whittaker
Content Visualization and Intermedia Representations (CVIR’98)

1996

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A Prosodic Analysis of Discourse Segments in Direction-Giving Monologues
Julia Hirschberg | Christine H. Nakatani
34th Annual Meeting of the Association for Computational Linguistics

1993

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Empirical Studies on the Disambiguation of Cue Phrases
Julia Hirschberg | Diane Litman
Computational Linguistics, Volume 19, Number 3, September 1993

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A Speech-First Model for Repair Detection and Correction
Christine Nakatani | Julia Hirschberg
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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A Speech-First Model for Repair Detection and Correction
Christine Nakatani | Julia Hirschberg
31st Annual Meeting of the Association for Computational Linguistics

1992

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Session 13: Prosody
Patti Price | Julia Hirschberg
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Intonational Features of Local and Global Discourse Structure
Julia Hirschberg | Barbara Grosz
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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Predicting Intonational Boundaries Automatically from Text: The ATIS Domain
Michelle Q. Wang | Julia Hirschberg
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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Disambiguating Cue Phrases in Text and Speech
Diane Litman | Julia Hirschberg
COLING 1990 Volume 2: Papers presented to the 13th International Conference on Computational Linguistics

1989

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Distinguishing Questions by Contour Speech Recognition Tasks
Julia Hirschberg
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

1988

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Assigning Intonational Features in Synthesized Spoken Directions
James Raymond Davis | Julia Hirschberg
26th Annual Meeting of the Association for Computational Linguistics

1987

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Now Let’s Talk About Now; Identifying Cue Phrases Intonationally
Julia Hirschberg | Diane Litman
25th Annual Meeting of the Association for Computational Linguistics

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NO TITLE
Julia Hirschberg
Theoretical Issues in Natural Language Processing 3

1986

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The Intonational Structuring of Discourse
Julia Hirschberg | Janet Pierrehumbert
24th Annual Meeting of the Association for Computational Linguistics

1984

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Toward a Redefinition of Yes/No Questions
Julia Hirschberg
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

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