Contact

Luke Zettlemoyer
CSE 534
lszcs.washington.edu
Artificial Intelligence, Machine Learning, Natural Language Processing

2018

NL2Bash: A corpus and semantic parser for natural language interface to the Linux operating system

X.Victoria Lin, C. Wang, L. Zettlemoyer, M.D. Ernst, LREC: Language Resources and Evaluation Conference, 2018.
https://github.com/TellinaTool/nl2bash dataset

Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum

O. Levy, K. Lee, N. FitzGerald, L. Zettlemoyer, Proceedings of the Association for Computational Linguistics (ACL), 2018.
 

Supervised Open Information Extraction

G. Stanovsky, J. Michael, L. Zettlemoyer, I. Dagan, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
 

Ultra-Fine Entity Typing

E. Choi, O. Levy, Y. Choi, L. Zettlemoyer, Proceedings of the Association for Computational Linguistics (ACL), 2018.
 

Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

L. He, K. Lee, O. Levy, L. Zettlemoyer, Proceedings of the Association for Computational Linguistics (ACL), 2018. *Best Paper Award, Honorable Mention*
 

Large-Scale QA-SRL Parsing

N. FitzGerald, J. Michael, L. He, L. Zettlemoyer, Proceedings of the Association for Computational Linguistics (ACL), 2018. *Best Paper Award, Honorable Mention*
 

Higher-Order Coreference Resolution with Coarse-to-Fine Inference

K. Lee, L. He, L. Zettlemoyer, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
 

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

M. Iyyer, J. Wieting, K. Gimpel, L. Zettlemoyer, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
 

Deep Contextualized Word Representations

M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018. *Best Paper Award*
 

Crowdsourcing Question-Answer Meaning Representations

J. Michael, G. Stanovsky, L. He, I. Dagan, L. Zettlemoyer, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
 

Deep RNNs Encode Soft Hierarchical Syntax

T. Blevins, O. Levy, L. Zettlemoyer, Proceedings of the Association for Computational Linguistics (ACL), 2018.
 

2017

Program synthesis from natural language using recurrent neural networks

X.Victoria Lin, C. Wang, D. Pang, K. Vu, L. Zettlemoyer, M.D. Ernst, University of Washington Department of Computer Science and Engineering:UW-CSE-17-03-01, 2017.
https://kirin.cs.washington.edu:8000/ Tellina website https://github.com/TellinaTool/tellina Tellina implementation

Commonly Uncommon: Semantic Sparsity in Situation Recognition

M. Yatskar, V. Ordonez, L. Zettlemoyer, A. Farhadi, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 6335-6344.
 

Recurrent Additive Networks

K. Lee, O. Levy, L. Zettlemoyer, arXiv:1705.07393 , 2017.
https://github.com/kentonl/ran CODE

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

M. Joshi, E. Choi, D. Weld, L. Zettlemoyer, Conference of the Association for Computational Linguistics (ACL), 2017.
http://nlp.cs.washington.edu/triviaqa/ DATA https://github.com/mandarjoshi90/triviaqa CODE

Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

I. Konstas, S. Iyer, M. Yatskar, Y. Choi, L. Zettlemoyer, Conference of the Association for Computational Linguistics (ACL), 2017.
http://www.ikonstas.net/index.php?page=demos# DEMO https://github.com/sinantie/NeuralAmr CODE

Zero-Shot Relation Extraction via Reading Comprehension

O. Levy, M. Seo, E. Choi, L. Zettlemoyer, Conference on Natural Language Learning (CoNLL), 2017.
 

Learning a Neural Semantic Parser from User Feedback

S. Iyer, Y. Konstas, A. Cheung, J. Krishnamurthy, L. Zettlemoyer, Conference of the Association of Computational Linguistics (ACL), 2017.
https://github.com/sriniiyer/nl2sql CODE

Deep Semantic Role Labeling: What Works and What's Next

L. He, K. Lee, M. Lewis, L. Zettlemoyer, Conference of the Association for Computational Linguistics (ACL), 2017.
https://github.com/luheng/deep_srl CODE

Commonly Uncommon: Semantic Sparsity in Situation Recognition

M. Yatskar, V. Ordóñez, L. Zettlemoyer, A. Farhadi, Computer Vision and Pattern Recognition (CVPR), 2017.
http://imsitu.org/demo/ DEMO

End-to-end Neural Coreference Resolution

K. Lee, L. He, M. Lewis, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.
https://github.com/kentonl/e2e-coref CODE https://kentonl.github.io/e2e-coref DEMO

2016

LSTM CCG Parsing

M. Lewis, K. Lee, L. Zettlemoyer, Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2016.
 

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

M. Yatskar, L. Zettlemoyer, A. Farhadi, Proceedings of the Conference of Computer Vision and Pattern Recognition (CVPR), 2016.
http://imsitu.org/ WEB http://imsitu.org/browse/ DATA http://imsitu.org/demo DEMO

Human-in-the-Loop Parsing

L. He, J. Michael, M. Lewis, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.
 

Summarizing Source Code using a Neural Attention Model

S. Iyer, Y. Konstas, A. Cheung, L. Zettlemoyer, Annual Meeting of the Association for Computational Linguistics (ACL), 2016.
https://github.com/sriniiyer/codenn CODE

Global Neural CCG Parsing with Optimality Guarantees

K. Lee, M. Lewis, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Process (EMNLP), 2016. *Best Paper Award*
 

Document-level Sentiment Inference with Social, Faction, and Discourse Context

E. Choi, H. Rashkin, L. Zettlemoyer, Y. Choi, Conference of the Association for Computational Linguistics (ACL), 2016.
 

A Theme-Rewriting Approach for Generating Algebra Word Problems

R. Koncel-Kedziorski, I. Konstas, L. Zettlemoyer, H. Hajishirzi, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.
 

Globally Coherent Text Generation with Neural Checklist Models

C. Kiddon, L. Zettlemoyer, Y. Choi, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.
 

2015

Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language

L. He, M. Lewis, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
https://dada.cs.washington.edu/qasrl/ CODE and DATA https://homes.cs.washington.edu/~luheng/files/qasrl_slides.pdf SLIDES

Robot Programming by Demonstration with Situated Spatial Language Understanding

M. Forbes, R.P.N. Rao, L. Zettlemoyer, M. Cakmak, International Conference on Robotics and Automation (ICRA), 2015.
 

Scalable Semantic Parsing with Partial Ontologies

E. Choi, T. Kwiatkowski, L. Zettlemoyer, Association for Computational Linguistics (ACL), 2015.
 

Personalized Mathematical Word Problem Generation

O. Polozov, E. O'Rourke, A.M. Smith, L. Zettlemoyer, S. Gulwani, Z. Popović, International Joint Conference on Artificial Intelligence (IJCAI), 2015.
 

Mise en Place: Unsupervised Interpretation of Instructional Recipes

C. Kiddon, G.Thandavam Ponnuraj, L. Zettlemoyer, Y. Choi, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
 

Joint A∗ CCG Parsing and Semantic Role Labeling

M. Lewis, L. He, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
https://github.com/mikelewis0/EasySRL CODE http://homes.cs.washington.edu/~mlewis/publications/emnlp2015_slides.pdf SLIDES http://lil.cs.washington.edu/easysrl/demo.cgi DEMO

Event Detection and Factuality Assessment with Non-Expert Supervision

K. Lee, Y. Artzi, Y. Choi, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
http://homes.cs.washington.edu/~kentonl/pub/lacz-emnlp.2015.poster.pdf POSTER http://homes.cs.washington.edu/~kentonl/pub/lacz-emnlp.2015.slides.pdf SLIDES https://bitbucket.org/kentonl/factuality-data DATA

Broad-coverage CCG Semantic Parsing with AMR

Y. Artzi, K. Lee, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015. *Best Paper Award*
http://yoavartzi.com/pub/alz-emnlp.2015.sup.pdf SUPPLEMENTARY MATERIAL http://yoavartzi.com/pub/alz-emnlp.2015.slides.pdf SLIDES

Extreme Extraction: Only One Hour per Relation

R. Hoffmann, L. Zettlemoyer, D.S. Weld, CoRR abs/1506.06418, 2015.
 

2014

Morpho-syntactic Lexical Generalization for CCG Semantic Parsing

A.X. Wang, T. Kwiatkowski, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
 

Learning to Automatically Solve Algebra Word Problems

N. Kushman, Y. Artzi, L. Zettlemoyer, R. Barzilay, Association for Computational Linguistics (ACL), 2014.
 

Open Question Answering Over Curated and Extracted Knowledge Bases

A. Fader, O. Etzioni, L. Zettlemoyer, Conference on Knowledge Discovery and Data Mining (KDD), 2014.
https://github.com/afader/oqa CODE https://github.com/afader/oqa DATA

See No Evil, Say No Evil: Description Generation from Densely Labeled Images

M. Yatskar, M. Galley, L. Vanderwende, L. Zettlemoyer, Joint Conference on Lexical and Computation Semantics (*SEM), 2014.
 

Context-dependent Semantic Parsing for Time Expressions

K. Lee, Y. Artzi, J. Dodge, L. Zettlemoyer, Association for Computational Linguistics (ACL), 2014.
http://lil.cs.washington.edu/uwtime/ DEMO https://bitbucket.org/kentonl/uwtime CODE https://bitbucket.org/kentonl/uwtime-standalone TOOL

Introduction to the special issue on learning semantics.

A. Bordes, L. Bottou, R. Collobert, D. Roth, J. Weston, L. Zettlemoyer, Machine Learning 94, 2014: 127–131.
 

Learning from Unscripted Deictic Gesture and Language for Human-Robot Interactions

C. Matuszek, L. Bo, L. Zettlemoyer, D. Fox, Conference on Artificial Intelligence (AAAI), 2014.
 

2013

Joint Coreference Resolution and Named-Entity Linking with Multi-pass Sieves

H. Hajishirzi, L. Zilles, D.S. Weld, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
 

Modeling Missing Data in Distant Supervision for Information Extraction.

A. Ritter, L. Zettlemoyer,  Mausam, O. Etzioni, Transactions of the Association for Computational Linguistics (TACL) 1, 2013: 367–378.
http://aritter.github.io/emnlp13.pptx SLIDES

Scaling semantic parsers with on-the-fly ontology matching

T. Kwiatkowski, E. Choi, Y. Artzi, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
 

Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions.

Y. Artzi, L. Zettlemoyer, Transactions of the Association for Computational Linguistics (TACL) 1, 2013: 49–62.
https://bitbucket.org/yoavartzi/spf CODE

Lightly Supervised Learning of Procedural Dialog Systems

S. Volkova, P. Choudhury, C. Quirk, B. Dolan, L.S. Zettlemoyer, Association for Computational Linguistics (ACL), 2013.
 

Paraphrase-Driven Learning for Open Question Answering

A. Fader, L. Zettlemoyer, O. Etzioni, Association for Computational Linguistics (ACL), 2013.
http://knowitall.cs.washington.edu/paralex/ CODE http://knowitall.cs.washington.edu/paralex/ DATA

Automatic Idiom Identification in Wiktionary

G. Muzny, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
https://bitbucket.org/muzny/wiktionaryidioms/downloads/ DATA https://bitbucket.org/muzny/wiktionaryidioms/ CODE

“Can you give me another word for hyperbaric?”: Improving speech translation using targeted clarification questions

N.Fazil Ayan, A. Mandal, M. Frandsen, J. Zheng, P. Blasco, A. Kathol, F. Béchet, B. Favre, A. Marin, T. Kwiatkowski, M. Ostendorf, L. Zettlemoyer, P. Salletmayr, J. Hirschberg, S. Stoyanchev, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2013.
 

3D Wikipedia: using online text to automatically label and navigate reconstructed geometry

B.C. Russell, R. Martin-Brualla, D.J. Butler, S.M. Seitz, L. Zettlemoyer, ACM Trans. Graph. (SIGGRAPH Asia) 32, 2013.
http://grail.cs.washington.edu/projects/label3d/ VIDEO

Learning Distributions over Logical Forms for Referring Expression Generation.

N. FitzGerald, Y. Artzi, L.S. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
https://bitbucket.org/nfitzgerald/genx-referring-expression-corpus DATA

Learning to Relate Literal and Sentimental Descriptions of Visual Properties.

M. Yatskar, S. Volkova, A. Celikyilmaz, B. Dolan, L.S. Zettlemoyer, North American Association for Computational Linguistics (NAACL), 2013.
 

2012

Discriminative Learning for Joint Template Filling

E. Minkov, L. Zettlemoyer, Association for Computational Linguistics (ACL), 2012: 845–853.
 

Learning to Parse Natural Language Commands to a Robot Control System

C. Matuszek, E. Herbst, L. Zettlemoyer, D. Fox, International Symposium on Experimental Robotics (ISER), 2012.
 

A Joint Model of Language and Perception for Grounded Attribute Learning

C. Matuszek, N. FitzGerald, L. Zettlemoyer, L. Bo, D. Fox, Proceedings of the International Conference on Machine Learning (ICML), 2012.
 

Learning STRIPS Operators from Noisy and Incomplete Observations

K. Mourão, L.S. Zettlemoyer, R.P.A. Petrick, M. Steedman, Conference on Uncertainty in Artificial Intelligence (UAI) abs/1210.4889, 2012.
 

RevMiner: an extractive interface for navigating reviews on a smartphone

J. Huang, O. Etzioni, L. Zettlemoyer, K. Clark, C. Lee, Proceedings of the ACM symposium on User interface software and technology (UIST), ACM, 2012: 3–12.
 

A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings

T. Kwiatkowski, S. Goldwater, L. Zettlemoyer,  Steedman, Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2012.
 

2011

Bootstrapping Semantic Parsers from Conversations

Y. Artzi, L. Zettlemoyer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2011.
 

Lexical Generalization in CCG Grammar Induction for Semantic Parsing

T. Kwiatkowski, L. Zettlemoyer, S. Goldwater, M. Steedman, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2011: 1512–1523.
 

Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

R. Hoffmann, C. Zhang, X. Ling, L. Zettlemoyer, D. Weld, Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2011.
http://homes.cs.washington.edu/~raphaelh/mr/index.html Source Code

2010

Inducing Probabilistic CCG Grammars from Logical Form with Higher-order Unification

T. Kwiatkowski, L. Zettlemoyer, S. Goldwater, M. Steedman, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
 

Reading Between the Lines: Learning to Map High-level Instructions to Commands

S.R.K. Branavan, L. Zettlemoyer, R. Barzilay, Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2010.
 

2009

Learning Context-dependent Mappings from Sentences to Logical Form

L. Zettlemoyer, M. Collins, Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2009.
 

Reinforcement Learning for Mapping Instructions to Actions

S.R.K. Branavan, H. Chen, L. Zettlemoyer, R. Barzilay, Proceeding of the Annual Meeting of the Association for Computational Linguistics (ACL), 2009. *Best Paper Award*
 

2008

A Generative Model for Parsing Natural Language to Meaning Representations

W. Lu, H.T. Ng, W.S. Lee, L. Zettlemoyer, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2008: 783–792.
 

Multi-agent filtering with infinitely nested beliefs

L. Zettlemoyer, B. Milch, L.P. Kaelbling, Neural Information Processing Systems (NIPS), 2008.
 

Lifted probabilistic inference with counting formulas

B. Milch, L. Zettlemoyer, K. Kersting, M. Haimes, L.P. Kaelbling, Proceedings of the National Conference on Artificial intelligence (AAAI), 2008: 1062–1068.
 

2007

Selective Phrase Pair Extraction for Improved Statistical Machine Translation

L.S. Zettlemoyer, R.C. Moore, Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2007.
http://homes.cs.washington.edu/~lsz/papers/zm-naacl07-slides.pdf SLIDES

Online Learning of Relaxed CCG Grammars for Parsing to Logical Form

L.S. Zettlemoyer, M. Collins, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2007.
http://homes.cs.washington.edu/~lsz/papers/zc-emnlp07-slides.pdf SLIDES

Learning Symbolic Models of Stochastic Domains

H.M. Pasula, L.S. Zettlemoyer, L.P. Kaelbling, Journal of Artificial Intelligence Research  29, 2007.
 

Learning Probabilistic Relational Dynamics for Multiple Tasks

A. Deshpande, B. Milch, L.S. Zettlemoyer, L.P. Kaelbling, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2007.
 

Logical Particle Filtering

L.S. Zettlemoyer, H.M. Pasula, L.P. Kaelbling, Proceedings of the Dagstuhl Seminar on Probabilistic, Logical, and Relational Learning, 2007.
http://homes.cs.washington.edu/~lsz/papers/zpk-dagstuhl07-slides.pdf SLIDES

2005

Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars

L.S. Zettlemoyer, M. Collins, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2005. *Best Paper Award*
http://homes.cs.washington.edu/~lsz/papers/zc-uai05-slides.pdf SLIDES

Learning Planning Rules in Noisy Stochastic Worlds

L.S. Zettlemoyer, H.M. Pasula, L.P. Kaelbling, Proceedings of the National Conference on Artificial Intelligence (AAAI), 2005.
 

2004

Learning Probabilistic Relational Planning Rules

H.M. Pasula, L.S. Zettlemoyer, L.P. Kaelbling, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2004.
 

2000

Visual generalization in programming by example

R.St. Amant, H. Lieberman, R. Potter, L.S. Zettlemoyer,  Communications of the ACM 43:3, 2000.
 

The user interface as an agent environment

R.St. Amant, L.S. Zettlemoyer, Proceedings of the International Conference on Autonomous Agents (Agents), 2000.
 

1999

IBOTS: Agent control through the user interface

L.S. Zettlemoyer, R.St. Amant, M.S. Dulberg, International Conference on Intelligent User Interfaces (IUI), 1999.
 

Towards Narrative-Centered Learning Environments

B.W. Mott, C.C. Callaway, L.S. Zettlemoyer, S.Y. Lee, J.C. Lester, Working Notes of the AAAI Fall Symposium on Narrative Intelligence, 1999.
 

Explanatory Lifelike Avatars: Performing User-Centered Tasks in 3D Learning Environments

J.C. Lester, L.S. Zettlemoyer, J.P. Grégoire, W.H. Bares, Proceedings of the International Conference on Autonomous Agents (Agents), 1999.
 

A visual medium for programmatic control of interactive applications

L.S. Zettlemoyer, R.St. Amant, Conference on Human Factors in Computing Systems (CHI), 1999.
 

Detecting and Correcting Misconceptions with Lifelike Avatars in 3D Learning Environments

J.P. Grégoire, L.S. Zettlemoyer, J.C. Lester, Proceedings of the World Conference on Artificial Intelligence in Education (AI-ED), 1999.
 

An Imprecise Mouse Gesture for the Fast Activation of Controls

M.S. Dulberg, R.St. Amant, L.S. Zettlemoyer, Proceedings of International Conference on Human-Computer Interaction (INTERACT), 1999.
 

1998

Task-Sensitive Cinematography Interfaces for Interactive 3D Learning Environments

W.H. Bares, L.S. Zettlemoyer, D.W. Rodriguez, J.C. Lester, Proceedings of the International Conference on Intelligent User Interfaces (IUI), 1998.
 

Habitable 3D Learning Environments for Situated Learning

W.H. Bares, L.S. Zettlemoyer, J.C. Lester, Proceedings of the International Conference on Intelligent Tutoring Systems (ITS), 1998.