Ingrid Zukerman


2019

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Influence of Time and Risk on Response Acceptability in a Simple Spoken Dialogue System
Andisheh Partovi | Ingrid Zukerman
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

We describe a longitudinal user study conducted in the context of a Spoken Dialogue System for a household robot, where we examined the influence of time displacement and situational risk on users’ preferred responses. To this effect, we employed a corpus of spoken requests that asked a robot to fetch or move objects in a room. In the first stage of our study, participants selected among four response types to these requests under two risk conditions: low and high. After some time, the same participants rated several responses to the previous requests — these responses were instantiated from the four response types. Our results show that participants did not rate highly their own response types; moreover, they rated their own response types similarly to different ones. This suggests that, at least in this context, people’s preferences at a particular point in time may not reflect their general attitudes, and that various reasonable response types may be equally acceptable. Our study also reveals that situational risk influences the acceptability of some response types.

2018

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The Context-Dependent Additive Recurrent Neural Net
Quan Hung Tran | Tuan Lai | Gholamreza Haffari | Ingrid Zukerman | Trung Bui | Hung Bui
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). Here, instead of relying solely on the information presented in the text, the learning agents have access to a strong external signal given to assist the learning process. In this paper, we propose a novel family of Recurrent Neural Network unit: the Context-dependent Additive Recurrent Neural Network (CARNN) that is designed specifically to address this type of problem. The experimental results on public datasets in the dialog problem (Babi dialog Task 6 and Frame), contextual language model (Switchboard and Penn Tree Bank) and question answering (Trec QA) show that our novel CARNN-based architectures outperform previous methods.

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Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
Xuanli He | Quan Tran | William Havard | Laurent Besacier | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the Australasian Language Technology Association Workshop 2018

In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)’s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.

2017

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A Generative Attentional Neural Network Model for Dialogue Act Classification
Quan Hung Tran | Gholamreza Haffari | Ingrid Zukerman
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a novel attentional technique and a label to label connection for sequence learning, akin to Hidden Markov Models. The experiments show that both of these innovations lead our model to outperform strong baselines for dialogue act classification on MapTask and Switchboard corpora. We further empirically analyse the effectiveness of each of the new innovations.

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A Hierarchical Neural Model for Learning Sequences of Dialogue Acts
Quan Hung Tran | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.

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Preserving Distributional Information in Dialogue Act Classification
Quan Hung Tran | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper introduces a novel training/decoding strategy for sequence labeling. Instead of greedily choosing a label at each time step, and using it for the next prediction, we retain the probability distribution over the current label, and pass this distribution to the next prediction. This approach allows us to avoid the effect of label bias and error propagation in sequence learning/decoding. Our experiments on dialogue act classification demonstrate the effectiveness of this approach. Even though our underlying neural network model is relatively simple, it outperforms more complex neural models, achieving state-of-the-art results on the MapTask and Switchboard corpora.

2016

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Inter-document Contextual Language model
Quan Hung Tran | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Corpus of Tables in Full-Text Biomedical Research Publications
Tatyana Shmanina | Ingrid Zukerman | Ai Lee Cheam | Thomas Bochynek | Lawrence Cavedon
Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)

The development of text mining techniques for biomedical research literature has received increased attention in recent times. However, most of these techniques focus on prose, while much important biomedical data reside in tables. In this paper, we present a corpus created to serve as a gold standard for the development and evaluation of techniques for the automatic extraction of information from biomedical tables. We describe the guidelines used for corpus annotation and the manner in which they were developed. The high inter-annotator agreement achieved on the corpus, and the generic nature of our annotation approach, suggest that the developed guidelines can serve as a general framework for table annotation in biomedical and other scientific domains. The annotated corpus and the guidelines are available at http://www.csse.monash.edu.au/research/umnl/data/index.shtml.

2014

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A Comparative Study of Weighting Schemes for the Interpretation of Spoken Referring Expressions
Su Nam Kim | Ingrid Zukerman | Thomas Kleinbauer | Masud Moshtaghi
Proceedings of the Australasian Language Technology Association Workshop 2014

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Challenges in Information Extraction from Tables in Biomedical Research Publications: a Dataset Analysis
Tatyana Shmanina | Lawrence Cavedon | Ingrid Zukerman
Proceedings of the Australasian Language Technology Association Workshop 2014

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Authorship Attribution with Topic Models
Yanir Seroussi | Ingrid Zukerman | Fabian Bohnert
Computational Linguistics, Volume 40, Issue 2 - June 2014

2013

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Evaluation of the Scusi? Spoken Language Interpretation System – A Case Study
Thomas Kleinbauer | Ingrid Zukerman | Su Nam Kim
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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A Noisy Channel Approach to Error Correction in Spoken Referring Expressions
Su Nam Kim | Ingrid Zukerman | Thomas Kleinbauer | Farshid Zavareh
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Impact of Corpus Diversity and Complexity on NER Performance
Tatyana Shmanina | Ingrid Zukerman | Antonio Jimeno Yepes | Lawrence Cavedon | Karin Verspoor
Proceedings of the Australasian Language Technology Association Workshop 2013 (ALTA 2013)

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Error Detection in Automatic Speech Recognition
Farshid Zavareh | Ingrid Zukerman | Su Nam Kim | Thomas Kleinbauer
Proceedings of the Australasian Language Technology Association Workshop 2013 (ALTA 2013)

2012

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Experimental Evaluation of a Lexicon- and Corpus-based Ensemble for Multi-way Sentiment Analysis
Minh Duc Cao | Ingrid Zukerman
Proceedings of the Australasian Language Technology Association Workshop 2012

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Authorship Attribution with Author-aware Topic Models
Yanir Seroussi | Fabian Bohnert | Ingrid Zukerman
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Authorship Attribution with Latent Dirichlet Allocation
Yanir Seroussi | Ingrid Zukerman | Fabian Bohnert
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

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In Situ Text Summarisation for Museum Visitors
Timothy Baldwin | Patrick Ye | Fabian Bohnert | Ingrid Zukerman
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

2010

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A Hierarchical Classifier Applied to Multi-way Sentiment Detection
Adrian Bickerstaffe | Ingrid Zukerman
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Interpreting Pointing Gestures and Spoken Requests – A Probabilistic, Salience-based Approach
Ingrid Zukerman | Gideon Kowadlo | Patrick Ye
Coling 2010: Posters

2009

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Towards the Interpretation of Utterance Sequences in a Dialogue System
Ingrid Zukerman | Patrick Ye | Kapil Kumar Gupta | Enes Makalic
Proceedings of the SIGDIAL 2009 Conference

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An Empirical Study of Corpus-Based Response Automation Methods for an E-mail-Based Help-Desk Domain
Yuval Marom | Ingrid Zukerman
Computational Linguistics, Volume 35, Number 4, December 2009

2006

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Proceedings of the Australasian Language Technology Workshop 2006
Lawrence Cavedon | Ingrid Zukerman
Proceedings of the Australasian Language Technology Workshop 2006

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Automating Help-desk Responses: A Comparative Study of Information-gathering Approaches
Yuval Marom | Ingrid Zukerman
Proceedings of the Workshop on Task-Focused Summarization and Question Answering

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Balancing Conflicting Factors in Argument Interpretation
Ingrid Zukerman | Michael Niemann | Sarah George
Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue

2005

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Book Review: Argumentation Machines: New Frontiers in Argumentation and Computation, edited by Chris Reed and Timothy J. Norman
Ingrid Zukerman
Computational Linguistics, Volume 31, Number 1, March 2005

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Exploring and Exploiting the Limited Utility of Captions in Recognizing Intention in Information Graphics
Stephanie Elzer | Sandra Carberry | Daniel Chester | Seniz Demir | Nancy Green | Ingrid Zukerman | Keith Trnka
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Filtering Speaker-Specific Words from Electronic Discussions
Ingrid Zukerman | Yuval Marom
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Lexical Paraphrasing for Document Retrieval and Node Identification
Ingrid Zukerman | Sarah George | Yingying Wen
Proceedings of the Second International Workshop on Paraphrasing

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An Information-theoretic Approach for Argument Interpretation
Sarah George | Ingrid Zukerman
Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue

2002

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A Minimum Message Length Approach for Argument Interpretation
Ingrid Zukerman | Sarah George
Proceedings of the Third SIGdial Workshop on Discourse and Dialogue

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Towards a Noise-Tolerant, Representation-Independent Mechanism for Argument Interpretation
Ingrid Zukerman | Sarah George
COLING 2002: The 19th International Conference on Computational Linguistics

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Lexical Query Paraphrasing for Document Retrieval
Ingrid Zukerman | Bhavani Raskutti
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Using Machine Learning Techniques to Interpret WH-questions
Ingrid Zukerman | Eric Horvitz
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

2000

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Towards the Generation of Rebuttals in a Bayesian Argumentation System
Nathalie Jitnah | Ingrid Zukerman | Richard McConachy | Sarah George
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

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Using Argumentation Strategies in Automated Argument Generation
Ingrid Zukerman | Richard McConachy | Sarah George
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

1998

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A Bayesian Approach to Automating Argumentation
Richard McConachy | Kevin B. Korb | Ingrid Zukerman
New Methods in Language Processing and Computational Natural Language Learning

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Extracting Phoneme Pronunciation Information from Corpora
Ian Thomas | Ingrid Zukerman | Bhavani Raskutti
New Methods in Language Processing and Computational Natural Language Learning

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Attention During Argument Generation and Presentation
Ingrid Zukerman | Richard McConachy | Kevin B. Korb
Natural Language Generation

1994

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Discourse Planning as an Optimization Process
Ingrid Zukerman | Richard McConachy
Proceedings of the Seventh International Workshop on Natural Language Generation

1991

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Current Research in Natural Language Generation
Ingrid Zukerman
Computational Linguistics, Volume 17, Number 3, September 1991

1990

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Generating Peripheral Rhetorical Devices by Consulting a User Model
Ingrid Zukerman
Proceedings of the Fifth International Workshop on Natural Language Generation