Lucy Vanderwende


2018

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What Action Causes This? Towards Naive Physical Action-Effect Prediction
Qiaozi Gao | Shaohua Yang | Joyce Chai | Lucy Vanderwende
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.

2017

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Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Nasrin Mostafazadeh | Chris Brockett | Bill Dolan | Michel Galley | Jianfeng Gao | Georgios Spithourakis | Lucy Vanderwende
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.

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Filling the Blanks (hint: plural noun) for Mad Libs Humor
Nabil Hossain | John Krumm | Lucy Vanderwende | Eric Horvitz | Henry Kautz
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.

2016

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A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
Nasrin Mostafazadeh | Nathanael Chambers | Xiaodong He | Devi Parikh | Dhruv Batra | Lucy Vanderwende | Pushmeet Kohli | James Allen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Visual Storytelling
Ting-Hao Kenneth Huang | Francis Ferraro | Nasrin Mostafazadeh | Ishan Misra | Aishwarya Agrawal | Jacob Devlin | Ross Girshick | Xiaodong He | Pushmeet Kohli | Dhruv Batra | C. Lawrence Zitnick | Devi Parikh | Lucy Vanderwende | Michel Galley | Margaret Mitchell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Generating Natural Questions About an Image
Nasrin Mostafazadeh | Ishan Misra | Jacob Devlin | Margaret Mitchell | Xiaodong He | Lucy Vanderwende
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures
Nasrin Mostafazadeh | Alyson Grealish | Nathanael Chambers | James Allen | Lucy Vanderwende
Proceedings of the Fourth Workshop on Events

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Story Cloze Evaluator: Vector Space Representation Evaluation by Predicting What Happens Next
Nasrin Mostafazadeh | Lucy Vanderwende | Wen-tau Yih | Pushmeet Kohli | James Allen
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

2015

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A Survey of Current Datasets for Vision and Language Research
Francis Ferraro | Nasrin Mostafazadeh | Ting-Hao Huang | Lucy Vanderwende | Jacob Devlin | Michel Galley | Margaret Mitchell
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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An AMR parser for English, French, German, Spanish and Japanese and a new AMR-annotated corpus
Lucy Vanderwende | Arul Menezes | Chris Quirk
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Deep Questions without Deep Understanding
Igor Labutov | Sumit Basu | Lucy Vanderwende
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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See No Evil, Say No Evil: Description Generation from Densely Labeled Images
Mark Yatskar | Michel Galley | Lucy Vanderwende | Luke Zettlemoyer
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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Book Reviews: Semantic Relations Between Nominals by Vivi Nastase, Preslav Nakov, Diarmuid Ó Séaghdha, and Stan Szpakowicz
Lucy Vanderwende
Computational Linguistics, Volume 40, Issue 2 - June 2014

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Annotating Clinical Events in Text Snippets for Phenotype Detection
Prescott Klassen | Fei Xia | Lucy Vanderwende | Meliha Yetisgen
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. NLP systems that analyze the narrative data embedded in clinical artifacts such as x-ray reports can help support early detection. In this paper, we consider the importance of identifying the change of state for events - in particular, clinical events that measure and compare the multiple states of a patient’s health across time. We propose a schema for event annotation comprised of five fields and create preliminary annotation guidelines for annotators to apply the schema. We then train annotators, measure their performance, and finalize our guidelines. With the complete guidelines, we then annotate a corpus of snippets extracted from chest x-ray reports in order to integrate the annotations as a new source of features for classification tasks.

2013

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Annotating Change of State for Clinical Events
Lucy Vanderwende | Fei Xia | Meliha Yetisgen-Yildiz
Workshop on Events: Definition, Detection, Coreference, and Representation

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Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading
Sumit Basu | Chuck Jacobs | Lucy Vanderwende
Transactions of the Association for Computational Linguistics, Volume 1

We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.

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Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Lucy Vanderwende | Hal Daumé III | Katrin Kirchhoff
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Probabilistic Frame Induction
Jackie Chi Kit Cheung | Hoifung Poon | Lucy Vanderwende
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Mind the Gap: Learning to Choose Gaps for Question Generation
Lee Becker | Sumit Basu | Lucy Vanderwende
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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MSR SPLAT, a language analysis toolkit
Chris Quirk | Pallavi Choudhury | Jianfeng Gao | Hisami Suzuki | Kristina Toutanova | Michael Gamon | Wen-tau Yih | Colin Cherry | Lucy Vanderwende
Proceedings of the Demonstration Session at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Statistical Section Segmentation in Free-Text Clinical Records
Michael Tepper | Daniel Capurro | Fei Xia | Lucy Vanderwende | Meliha Yetisgen-Yildiz
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Automatically segmenting and classifying clinical free text into sections is an important first step to automatic information retrieval, information extraction and data mining tasks, as it helps to ground the significance of the text within. In this work we describe our approach to automatic section segmentation of clinical records such as hospital discharge summaries and radiology reports, along with section classification into pre-defined section categories. We apply machine learning to the problems of section segmentation and section classification, comparing a joint (one-step) and a pipeline (two-step) approach. We demonstrate that our systems perform well when tested on three data sets, two for hospital discharge summaries and one for radiology reports. We then show the usefulness of section information by incorporating it in the task of extracting comorbidities from discharge summaries.

2011

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MSR-NLP Entry in BioNLP Shared Task 2011
Chris Quirk | Pallavi Choudhury | Michael Gamon | Lucy Vanderwende
Proceedings of BioNLP Shared Task 2011 Workshop

2010

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Joint Inference for Knowledge Extraction from Biomedical Literature
Hoifung Poon | Lucy Vanderwende
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Mari Ostendorf | Michael Collins | Shri Narayanan | Douglas W. Oard | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Exploring Content Models for Multi-Document Summarization
Aria Haghighi | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Mari Ostendorf | Michael Collins | Shri Narayanan | Douglas W. Oard | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Using Contextual Speller Techniques and Language Modeling for ESL Error Correction
Michael Gamon | Jianfeng Gao | Chris Brockett | Alexandre Klementiev | William B. Dolan | Dmitriy Belenko | Lucy Vanderwende
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources
Krysta Svore | Lucy Vanderwende | Christopher Burges
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Proceedings of the Workshop on Task-Focused Summarization and Question Answering
Tat-Seng Chua | Jade Goldstein | Simone Teufel | Lucy Vanderwende
Proceedings of the Workshop on Task-Focused Summarization and Question Answering

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Effectively Using Syntax for Recognizing False Entailment
Rion Snow | Lucy Vanderwende | Arul Menezes
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

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MindNet: An Automatically-Created Lexical Resource
Lucy Vanderwende | Gary Kacmarcik | Hisami Suzuki | Arul Menezes
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations

2004

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Using the Penn Treebank to Evaluate Non-Treebank Parsers
Eric K. Ringger | Robert C. Moore | Eugene Charniak | Lucy Vanderwende | Hisami Suzuki
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Using N-Grams To Understand the Nature of Summaries
Michele Banko | Lucy Vanderwende
Proceedings of HLT-NAACL 2004: Short Papers

1998

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MindNet: acquiring and structuring semantic information from text
Stephen D. Richardson | William B. Dolan | Lucy Vanderwende
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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MindNet: Acquiring and Structuring Semantic Information from Text
Stephen D. Richardson | William B. Dolan | Lucy Vanderwende
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1994

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Algorithm for Automatic Interpretation of Noun Sequences
Lucy Vanderwende
COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics

1992

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Structural Patterns vs. String Patterns for Extracting Semantic Information from Dictionaries
Simonetta Montemagni | Lucy Vanderwende
COLING 1992 Volume 2: The 15th International Conference on Computational Linguistics