Julia Hockenmaier


2020

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Learning to execute instructions in a Minecraft dialogue
Prashant Jayannavar | Anjali Narayan-Chen | Julia Hockenmaier
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The Minecraft Collaborative Building Task is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment. We define the subtask of predicting correct action sequences (block placements and removals) in a given game context, and show that capturing B’s past actions as well as B’s perspective leads to a significant improvement in performance on this challenging language understanding problem.

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A Multi-Perspective Architecture for Semantic Code Search
Rajarshi Haldar | Lingfei Wu | JinJun Xiong | Julia Hockenmaier
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code–text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.

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University of Illinois Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Marc Canby | Aidana Karipbayeva | Bryan Lunt | Sahand Mozaffari | Charlotte Yoder | Julia Hockenmaier
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The objective of this shared task is to produce an inflected form of a word, given its lemma and a set of tags describing the attributes of the desired form. In this paper, we describe a transformer-based model that uses a bidirectional decoder to perform this task, and evaluate its performance on the 90 languages and 18 language families used in this task.

2019

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Phrase Grounding by Soft-Label Chain Conditional Random Field
Jiacheng Liu | Julia Hockenmaier
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image. Although there are clear dependencies between how different mentions of the same caption should be grounded, previous structured prediction methods that aim to capture such dependencies need to resort to approximate inference or non-differentiable losses. In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions. In contrast to standard sequence labeling tasks, the phrase grounding task is defined such that there may be multiple correct candidate regions. To address this multiplicity of gold labels, we define so-called Soft-Label Chain CRFs, and present an algorithm that enables convenient end-to-end training. Our method establishes a new state-of-the-art on phrase grounding on the Flickr30k Entities dataset. Analysis shows that our model benefits both from the entity dependencies captured by the CRF and from the soft-label training regime. Our code is available at github.com/liujch1998/SoftLabelCCRF

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Collaborative Dialogue in Minecraft
Anjali Narayan-Chen | Prashant Jayannavar | Julia Hockenmaier
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. Since computer games allow us to simulate such tasks without the need for physical robots, we define a Minecraft-based collaborative building task in which one player (A, the Architect) is shown a target structure and needs to instruct the other player (B, the Builder) to build this structure. Both players interact via a chat interface. A can observe B but cannot place blocks. We present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. As a first step towards our goal of developing fully interactive agents for this task, we consider the subtask of Architect utterance generation, and show how challenging it is.

2018

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Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Ellen Riloff | David Chiang | Julia Hockenmaier | Jun’ichi Tsujii
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

2017

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Learning to Predict Denotational Probabilities For Modeling Entailment
Alice Lai | Julia Hockenmaier
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.

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Natural Language Inference from Multiple Premises
Alice Lai | Yonatan Bisk | Julia Hockenmaier
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard textual entailment.

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Towards Problem Solving Agents that Communicate and Learn
Anjali Narayan-Chen | Colin Graber | Mayukh Das | Md Rakibul Islam | Soham Dan | Sriraam Natarajan | Janardhan Rao Doppa | Julia Hockenmaier | Martha Palmer | Dan Roth
Proceedings of the First Workshop on Language Grounding for Robotics

Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI. These agents need to translate between utterances and actionable meaning representations that can be interpreted by task-specific problem solvers in a context-dependent manner. They should also be able to learn such actionable interpretations for new predicates on the fly. We define an agent architecture for this scenario and present a series of experiments in the Blocks World domain that illustrate how our architecture supports language learning and problem solving in this domain.

2016

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Evaluating Induced CCG Parsers on Grounded Semantic Parsing
Yonatan Bisk | Siva Reddy | John Blitzer | Julia Hockenmaier | Mark Steedman
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Focused Evaluation for Image Description with Binary Forced-Choice Tasks
Micah Hodosh | Julia Hockenmaier
Proceedings of the 5th Workshop on Vision and Language

2015

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Probing the Linguistic Strengths and Limitations of Unsupervised Grammar Induction
Yonatan Bisk | Julia Hockenmaier
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)

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Labeled Grammar Induction with Minimal Supervision
Yonatan Bisk | Christos Christodoulopoulos | Julia Hockenmaier
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Illinois-LH: A Denotational and Distributional Approach to Semantics
Alice Lai | Julia Hockenmaier
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young | Alice Lai | Micah Hodosh | Julia Hockenmaier
Transactions of the Association for Computational Linguistics, Volume 2

We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.

2013

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Proceedings of the Workshop on Vision and Natural Language Processing
Julia Hockenmaier | Tamara Berg
Proceedings of the Workshop on Vision and Natural Language Processing

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Proceedings of the Seventeenth Conference on Computational Natural Language Learning
Julia Hockenmaier | Sebastian Riedel
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

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An HDP Model for Inducing Combinatory Categorial Grammars
Yonatan Bisk | Julia Hockenmaier
Transactions of the Association for Computational Linguistics, Volume 1

We introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text. It achieves state of the art performance on a number of languages, and induces linguistically plausible lexicons.

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Proceedings of the 2013 NAACL HLT Student Research Workshop
Annie Louis | Richard Socher | Julia Hockenmaier | Eric K. Ringger
Proceedings of the 2013 NAACL HLT Student Research Workshop

2012

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Beefmoves: Dissemination, Diversity, and Dynamics of English Borrowings in a German Hip Hop Forum
Matt Garley | Julia Hockenmaier
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Induction of Linguistic Structure with Combinatory Categorial Grammars
Yonatan Bisk | Julia Hockenmaier
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

2010

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Proceedings of the NAACL HLT 2010 Student Research Workshop
Julia Hockenmaier | Diane Litman | Adriane Boyd | Mahesh Joshi | Frank Rudzicz
Proceedings of the NAACL HLT 2010 Student Research Workshop

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The Future Role of Language Resources for Natural Language Parsing (We Won’t Be Able to Rely on Pierre Vinken Rorever... or Will We Have to?)
Julia Hockenmaier
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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Collecting Image Annotations Using Amazon’s Mechanical Turk
Cyrus Rashtchian | Peter Young | Micah Hodosh | Julia Hockenmaier
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Cross-Caption Coreference Resolution for Automatic Image Understanding
Micah Hodosh | Peter Young | Cyrus Rashtchian | Julia Hockenmaier
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Normal-form parsing for Combinatory Categorial Grammars with generalized composition and type-raising
Julia Hockenmaier | Yonatan Bisk
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Shallow Information Extraction from Medical Forum Data
Parikshit Sondhi | Manish Gupta | ChengXiang Zhai | Julia Hockenmaier
Coling 2010: Posters

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Citation Author Topic Model in Expert Search
Yuancheng Tu | Nikhil Johri | Dan Roth | Julia Hockenmaier
Coling 2010: Posters

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Wide-Coverage NLP with Linguistically Expressive Grammars
Julia Hockenmaier | Yusuke Miyao | Josef van Genabith
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

2008

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Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Johan Bos | Edward Briscoe | Aoife Cahill | John Carroll | Stephen Clark | Ann Copestake | Dan Flickinger | Josef van Genabith | Julia Hockenmaier | Aravind Joshi | Ronald Kaplan | Tracy Holloway King | Sandra Kuebler | Dekang Lin | Jan Tore Lønning | Christopher Manning | Yusuke Miyao | Joakim Nivre | Stephan Oepen | Kenji Sagae | Nianwen Xue | Yi Zhang
Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation

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Non-local scrambling: the equivalence of TAG and CCG revisited
Julia Hockenmaier | Peter Young
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

2007

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ACL 2007 Workshop on Deep Linguistic Processing
Timothy Baldwin | Mark Dras | Julia Hockenmaier | Tracy Holloway King | Gertjan van Noord
ACL 2007 Workshop on Deep Linguistic Processing

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The Impact of Deep Linguistic Processing on Parsing Technology
Timothy Baldwin | Mark Dras | Julia Hockenmaier | Tracy Holloway King | Gertjan van Noord
Proceedings of the Tenth International Conference on Parsing Technologies

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CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Julia Hockenmaier | Mark Steedman
Computational Linguistics, Volume 33, Number 3, September 2007

2006

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Creating a CCGbank and a Wide-Coverage CCG Lexicon for German
Julia Hockenmaier
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Protein folding and chart parsing
Julia Hockenmaier | Aravind K. Joshi | Ken A. Dill
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Priming Effects in Combinatory Categorial Grammar
David Reitter | Julia Hockenmaier | Frank Keller
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2004

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Wide-Coverage Semantic Representations from a CCG Parser
Johan Bos | Stephen Clark | Mark Steedman | James R. Curran | Julia Hockenmaier
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Identifying Semantic Roles Using Combinatory Categorial Grammar
Daniel Gildea | Julia Hockenmaier
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

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Bootstrapping statistical parsers from small datasets
Mark Steedman | Miles Osborne | Anoop Sarkar | Stephen Clark | Rebecca Hwa | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
10th Conference of the European Chapter of the Association for Computational Linguistics

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Parsing with Generative Models of Predicate-Argument Structure
Julia Hockenmaier
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Example Selection for Bootstrapping Statistical Parsers
Mark Steedman | Rebecca Hwa | Stephen Clark | Miles Osborne | Anoop Sarkar | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

2002

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Building Deep Dependency Structures using a Wide-Coverage CCG Parser
Stephen Clark | Julia Hockenmaier | Mark Steedman
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Generative Models for Statistical Parsing with Combinatory Categorial Grammar
Julia Hockenmaier | Mark Steedman
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Acquiring Compact Lexicalized Grammars from a Cleaner Treebank
Julia Hockenmaier | Mark Steedman
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

1998

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Error-Driven Learning of Chinese Word Segmentation
Julia Hockenmaier | Chris Brew
Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation