Jennifer Chu-Carroll

Also published as: Jennifer Chu


2020

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To Test Machine Comprehension, Start by Defining Comprehension
Jesse Dunietz | Greg Burnham | Akash Bharadwaj | Owen Rambow | Jennifer Chu-Carroll | Dave Ferrucci
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension—a “Template of Understanding”—for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.

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GLUCOSE: GeneraLized and COntextualized Story Explanations
Nasrin Mostafazadeh | Aditya Kalyanpur | Lori Moon | David Buchanan | Lauren Berkowitz | Or Biran | Jennifer Chu-Carroll
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement. This paper details two concrete contributions. First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations. Using this platform, we collected a total of ~670K specific statements and general rules that capture implicit commonsense knowledge about everyday situations. Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE’s rich inferential content. However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans’ mental models.

2018

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Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
Srinivas Bangalore | Jennifer Chu-Carroll | Yunyao Li
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

2012

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Multi-Dimensional Feature Merger for Question Answering
Apoorv Agarwal | J. William Murdock | Jennifer Chu-Carroll | Adam Lally | Aditya Kalyanpur
Proceedings of COLING 2012

2011

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Using Syntactic and Semantic Structural Kernels for Classifying Definition Questions in Jeopardy!
Alessandro Moschitti | Jennifer Chu-Carroll | Siddharth Patwardhan | James Fan | Giuseppe Riccardi
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2006

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Proceedings of the Human Language Technology Conference of the NAACL, Main Conference
Robert C. Moore | Jeff Bilmes | Jennifer Chu-Carroll | Mark Sanderson
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Robert C. Moore | Jeff Bilmes | Jennifer Chu-Carroll | Mark Sanderson
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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Answering the question you wish they had asked: The impact of paraphrasing for Question Answering
Pablo Duboue | Jennifer Chu-Carroll
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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Improving QA Accuracy by Question Inversion
John Prager | Pablo Duboue | Jennifer Chu-Carroll
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2004

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Question Answering Using Constraint Satisfaction: QA-By-Dossier-With-Contraints
John Prager | Jennifer Chu-Carroll | Krzysztof Czuba
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2003

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In Question Answering, Two Heads Are Better Than One
Jennifer Chu-Carroll | Krzysztof Czuba | John Prager | Abraham Ittycheriah
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

2002

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A Hybrid Approach to Natural Language Web Search
Jennifer Chu-Carroll | John Prager | Yael Ravin | Christian Cesar
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

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A Machine-Learning Approach to Introspection in a Question Answering System
Krzysztof Czuba | John Prager | Jennifer Chu-Carroll
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

2000

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MIMIC: An Adaptive Mixed Initiative Spoken Dialogue System for Information Queries
Jennifer Chu-Carroll
Sixth Applied Natural Language Processing Conference

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Evaluating Automatic Dialogue Strategy Adaptation for a Spoken Dialogue System
Jennifer Chu-Carroll
1st Meeting of the North American Chapter of the Association for Computational Linguistics

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Using Dialogue Representations for Concept-to-Speech Generation
Christine H. Nakatani | Jennifer Chu-Carroll
ANLP-NAACL 2000 Workshop: Conversational Systems

1999

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Vector-based Natural Language Call Routing
Jennifer Chu-Carroll | Bob Carpenter
Computational Linguistics, Volume 25, Number 3, September 1999

1998

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Dialogue Management in Vector-Based Call Routing
Jennifer Chu-Carroll | Bob Carpenter
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Collaborative Response Generation in Planning Dialogues
Jennifer Chu-Carroll | Sandra Carberry
Computational-Linguistics, Volume 24, Number 3, September 1998

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Dialogue Management in Vector-Based Call Routing
Jennifer Chu-Carroll | Bob Carpenter
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

1997

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Tracking Initiative in Collaborative Dialogue Interactions
Jennifer Chu-Carroll | Michael K. Brown
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

1995

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Response Generation in Collaborative Negotiation
Jennifer Chu-Carroll | Sandra Carberry
33rd Annual Meeting of the Association for Computational Linguistics

1993

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Responding to User Queries in a Collaborative Environment
Jennifer Chu
31st Annual Meeting of the Association for Computational Linguistics

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Rhetorical Relations: Necessary But Not Sufficient
Sandra Carberry | Jennifer Chu | Nancy Green | Lynn Lambert
Intentionality and Structure in Discourse Relations