Luke Zettlemoyer

发表

RoBERTa: A Robustly Optimized BERT Pretraining Approach pdf

Omer Levy, Danqi Chen, Veselin Stoyanov, 2019, ArXiv.

Learning Symbolic Models of Stochastic Domains

L. P. Kaelbling, H. M. Pasula, L. S. Zettlemoyer, 2007, J. Artif. Intell. Res..

AllenNLP: A Deep Semantic Natural Language Processing Platform pdf

Luke S. Zettlemoyer, Michael Schmitz, Pradeep Dasigi, 2018, ArXiv.

Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

Luke S. Zettlemoyer, Yoav Artzi, Luke Zettlemoyer, 2013, TACL.

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

Luke S. Zettlemoyer, Michael Collins, Luke Zettlemoyer, 2005, UAI.

Reinforcement Learning for Mapping Instructions to Actions

Luke S. Zettlemoyer, Regina Barzilay, S. R. K. Branavan, 2009, ACL.

A Generative Model for Parsing Natural Language to Meaning Representations

Hwee Tou Ng, Luke S. Zettlemoyer, Wei Lu, 2008, EMNLP.

Online Learning of Relaxed CCG Grammars for Parsing to Logical Form

Luke S. Zettlemoyer, Michael Collins, Luke Zettlemoyer, 2007, EMNLP.

A Joint Model of Language and Perception for Grounded Attribute Learning

Luke S. Zettlemoyer, Dieter Fox, Cynthia Matuszek, 2012, ICML.

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

Ali Farhadi, Luke S. Zettlemoyer, Mark Yatskar, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Commonly Uncommon: Semantic Sparsity in Situation Recognition

Ali Farhadi, Luke S. Zettlemoyer, Vicente Ordonez, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Cloze-driven Pretraining of Self-attention Networks

Luke S. Zettlemoyer, Sergey Edunov, Alexei Baevski, 2019, EMNLP.

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

Eunsol Choi, Luke S. Zettlemoyer, Daniel S. Weld, 2017, ACL.

Dissecting Contextual Word Embeddings: Architecture and Representation

Luke S. Zettlemoyer, Wen-tau Yih, Matthew E. Peters, 2018, EMNLP.

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

Luke Zettlemoyer, R. Barzilay, 2010 .

Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection

Noah A. Smith, Suchin Gururangan, Dallas Card, 2022, EMNLP.

Learning to Parse Natural Language Commands to a Robot Control System

Luke S. Zettlemoyer, Dieter Fox, Evan Herbst, 2012, ISER.

Multi-Label Learning with Posterior Regularization

Xi Victoria Lin, Luke Zettlemoyer, B. Taskar, 2014 .

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

Mark Steedman, Luke S. Zettlemoyer, Sharon Goldwater, 2010, EMNLP.

End-to-end Neural Coreference Resolution pdf

Luke S. Zettlemoyer, Kenton Lee, Luheng He, 2017, EMNLP.

Open question answering over curated and extracted knowledge bases

Oren Etzioni, Luke Zettlemoyer, Anthony Fader, 2014, KDD.

Multi-Agent Filtering with Infinitely Nested Beliefs

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Brian Milch, 2008, NIPS.

3D Wikipedia

Luke S. Zettlemoyer, Steven M. Seitz, Ricardo Martin-Brualla, 2013, ACM Trans. Graph..

Reading between the Lines: Learning to Map High-Level Instructions to Commands

Luke S. Zettlemoyer, Regina Barzilay, S. R. K. Branavan, 2010, ACL.

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

Luke S. Zettlemoyer, Leila Zilles, Daniel S. Weld, 2013, EMNLP.

Unsupervised Cross-lingual Representation Learning at Scale

Veselin Stoyanov, Vishrav Chaudhary, Guillaume Wenzek, 2019, ACL.

Zero-Shot Relation Extraction via Reading Comprehension pdf

Omer Levy, Eunsol Choi, Luke S. Zettlemoyer, 2017, CoNLL.

Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

Luke S. Zettlemoyer, Daniel S. Weld, Xiao Ling, 2011, ACL.

Semantic Parsing with Combinatory Categorial Grammars

Luke S. Zettlemoyer, Yoav Artzi, Nicholas FitzGerald, 2013, ACL.

Learning to Automatically Solve Algebra Word Problems

Luke S. Zettlemoyer, Regina Barzilay, Yoav Artzi, 2014, ACL.

Event Detection and Factuality Assessment with Non-Expert Supervision

Yejin Choi, Luke S. Zettlemoyer, Kenton Lee, 2015, EMNLP.

LSTM CCG Parsing

Luke S. Zettlemoyer, Kenton Lee, Mike Lewis, 2016, NAACL.

Bootstrapping Semantic Parsers from Conversations

Luke S. Zettlemoyer, Yoav Artzi, Luke Zettlemoyer, 2011, EMNLP.

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

Omer Levy, Luke S. Zettlemoyer, Kenton Lee, 2018, ACL.

Context-dependent Semantic Parsing for Time Expressions

Luke S. Zettlemoyer, Kenton Lee, Yoav Artzi, 2014, ACL.

Robot Programming by Demonstration with situated spatial language understanding

Maya Cakmak, Rajesh P. N. Rao, Luke S. Zettlemoyer, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Syntactic Scaffolds for Semantic Structures pdf

Luke S. Zettlemoyer, Noah A. Smith, Kenton Lee, 2018, EMNLP.

Deep RNNs Encode Soft Hierarchical Syntax pdf

Omer Levy, Luke S. Zettlemoyer, Terra Blevins, 2018, ACL.

Evaluating Gender Bias in Machine Translation pdf

Noah A. Smith, Gabriel Stanovsky, Luke Zettlemoyer, 2019, ACL.

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks pdf

Luke S. Zettlemoyer, Kevin Gimpel, Mohit Iyyer, 2018, NAACL.

Learning STRIPS Operators from Noisy and Incomplete Observations pdf

Mark Steedman, Luke S. Zettlemoyer, Ronald P. A. Petrick, 2012, UAI.

Summarizing Source Code using a Neural Attention Model

Alvin Cheung, Luke S. Zettlemoyer, Ioannis Konstas, 2016, ACL.

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

Frédéric Béchet, Arindam Mandal, Andreas Kathol, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

COMPOSING INFORMATION EXTRACTION, SEMANTIC PARSING AND TRACTABLE INFERENCE FOR DEEP NLP UNIVERSITY OF WASHINGTON MAY 2018 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED

Luke Zettlemoyer, Hannaneh Hajishirzi, 2018 .

Learning to Parse Natural Language to a Robot Execution System

Luke Zettlemoyer, D. Fox, E. Herbst, 2012 .

Syntactic Sca ff olds for Semantic Structures

Noah A. Smith, Luke Zettlemoyer, Kenton Lee, 2018 .

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

Luke S. Zettlemoyer, Kenton Lee, Luheng He, 2017, ACL.

Zero-shot Entity Linking with Dense Entity Retrieval

Luke Zettlemoyer, Martin Josifoski, Ledell Yu Wu, 2019, ArXiv.

Mapping Language to Code in Programmatic Context

Luke Zettlemoyer, Alvin Cheung, Srini Iyer, 2018, EMNLP.

Prompt-free and Efficient Few-shot Learning with Language Models

Rabeeh Karimi Mahabadi, Luke Zettlemoyer, J. Henderson, 2022, ACL.

Learning Context-Dependent Mappings from Sentences to Logical Form

Luke S. Zettlemoyer, Michael Collins, Luke Zettlemoyer, 2009, ACL.

Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders

Terra Blevins, Luke Zettlemoyer, Luke Zettlemoyer, 2020, ACL.

Toward Unconstrained Gesture and Language Interfaces

Luke Zettlemoyer, D. Fox, Liefeng Bo, 2013 .

Towards Narrative-Centered Learning Environments

James C. Lester, Charles B. Callaway, Seung Y. Lee, 1999 .

OPT: Open Pre-trained Transformer Language Models

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2022, ArXiv.

Reasoning about Large Populations with Lifted Probabilistic Inference

Leslie Pack Kaelbling, Kristian Kersting, Luke Zettlemoyer, 2007, NIPS 2007.

An Imprecise Mouse Gesture for the Fast Activation of Controls

Luke S. Zettlemoyer, Robert St. Amant, Martin S. Dulberg, 1999, INTERACT.

MetaICL: Learning to Learn In Context

M. Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi, 2021, NAACL.

Mise en Place: Unsupervised Interpretation of Instructional Recipes

Luke Zettlemoyer, Chloé Kiddon, Ganesa Thandavam Ponnuraj, 2015, EMNLP 2015.

Learning Planning Rules in Stochastic Worlds

Leslie Pack Kaelbling, Luke Zettlemoyer, Hanna Pasula, 2005, AAAI 2005.

DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions pdf

Luke Zettlemoyer, Mandar Joshi, Weijia Shi, 2021, ArXiv.

Improving Passage Retrieval with Zero-Shot Question Generation

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, ArXiv.

Few-shot Mining of Naturally Occurring Inputs and Outputs

Daniel S. Weld, M. Lewis, Luke Zettlemoyer, 2022, ArXiv.

Nearest Neighbor Zero-Shot Inference

Luke Zettlemoyer, Julian Michael, Suchin Gururangan, 2022, Conference on Empirical Methods in Natural Language Processing.

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Luke Zettlemoyer, K. Tirumala, Aram H. Markosyan, 2022, 2205.10770.

8-bit Optimizers via Block-wise Quantization

M. Lewis, Luke Zettlemoyer, Sam Shleifer, 2021, ICLR.

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Luke Zettlemoyer, D. Fox, Daniel Gordon, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Questions Are All You Need to Train a Dense Passage Retriever

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, ArXiv.

Reading between the lines

Regina Barzilay, Luke Zettlemoyer, S.R.K. Branavan, 2010, Annual Meeting of the Association for Computational Linguistics.

On the Role of Bidirectionality in Language Model Pre-Training

Naman Goyal, Jingfei Du, Luke Zettlemoyer, 2022, ArXiv.

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Luke Zettlemoyer, Ari Holtzman, Yejin Choi, 2021, EMNLP.

A practical perception substrate for cognitive modeling in HCI

Mark O. Riedl, Luke Zettlemoyer, Luke Zettlemoyer, 1999 .

Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

Noah A. Smith, Noah A. Smith, M. Lewis, 2022, ArXiv.

Generalization through Memorization: Nearest Neighbor Language Models

Omer Levy, M. Lewis, Luke Zettlemoyer, 2019, ICLR.

LegoNN: Building Modular Encoder-Decoder Models

M. Lewis, Luke Zettlemoyer, Abdel-rahman Mohamed, 2022, ArXiv.

OPT: Open Pre-trained Transformer Language Models

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2022, ArXiv.

Natural Language to Code Translation with Execution

Sida I. Wang, Luke Zettlemoyer, Daniel Fried, 2022, ArXiv.

Mega: Moving Average Equipped Gated Attention

Luke Zettlemoyer, Xuezhe Ma, Junxian He, 2022, International Conference on Learning Representations.

Improving Policy Learning via Language Dynamics Distillation

Luke Zettlemoyer, Edward Grefenstette, Victor Zhong, 2022, NeurIPS.

Combinatory Categorial Grammars for Robust Natural Language Processing

James R. Curran, Stephen R. Clark, Michael Lewis, 2012 .

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Dragomir R. Radev, Noah A. Smith, Sida I. Wang, 2022, EMNLP.

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Luke Zettlemoyer, Ari Holtzman, Yejin Choi, 2021, EMNLP.

Few-shot Learning with Multilingual Language Models

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2021, ArXiv.

On the Role of Bidirectionality in Language Model Pre-Training

Naman Goyal, Jingfei Du, Luke Zettlemoyer, 2022, EMNLP.

CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation

Luke Zettlemoyer, Hannaneh Hajishirzi, Tanay Dixit, 2022, EMNLP.

Efficient Large Scale Language Modeling with Mixtures of Experts

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2021, EMNLP.

Unsupervised Cross-lingual Representation Learning at Scale

Myle Ott, Naman Goyal, Luke Zettlemoyer, 2019, ACL.

Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models

Luke Zettlemoyer, Hila Gonen, Terra Blevins, 2022, EMNLP.

Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models

Luke Zettlemoyer, Terra Blevins, 2022, EMNLP.

Few-shot Learning with Multilingual Generative Language Models

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2021, EMNLP.

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Luke Zettlemoyer, K. Tirumala, Aram H. Markosyan, 2022, Neural Information Processing Systems.

Questions Are All You Need to Train a Dense Passage Retriever

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, ArXiv.

Prompting Language Models for Linguistic Structure

Luke Zettlemoyer, Hila Gonen, Terra Blevins, 2022, ArXiv.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

M. Lewis, Luke Zettlemoyer, Tim Dettmers, 2022, ArXiv.

Retrieval-Augmented Multimodal Language Modeling

M. Lewis, Luke Zettlemoyer, J. Leskovec, 2022, ArXiv.

Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?

Luke Zettlemoyer, Ari Holtzman, Hila Gonen, 2022, EMNLP.

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

Luke Zettlemoyer, Iz Beltagy, Hannaneh Hajishirzi, 2022, ArXiv.

Nonparametric Masked Language Modeling

M. Lewis, Luke Zettlemoyer, Sewon Min, 2022, Annual Meeting of the Association for Computational Linguistics.

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

Luke Zettlemoyer, Daniel Fried, Tao Yu, 2022, ICML.

Selective Annotation Makes Language Models Better Few-Shot Learners

Noah A. Smith, Chen Henry Wu, Luke Zettlemoyer, 2022, ICLR.

Nearest Neighbor Zero-Shot Inference

Luke Zettlemoyer, Julian Michael, Suchin Gururangan, 2022, EMNLP.

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Luke Zettlemoyer, Ari Holtzman, Yejin Choi, 2021, EMNLP.

In-context Examples Selection for Machine Translation

M. Lewis, Luke Zettlemoyer, Marjan Ghazvininejad, 2022, ACL.

CREPE: Open-Domain Question Answering with False Presuppositions

Luke Zettlemoyer, Hannaneh Hajishirzi, Sewon Min, 2022, ArXiv.

The case for 4-bit precision: k-bit Inference Scaling Laws

Luke Zettlemoyer, Tim Dettmers, 2022, ArXiv.

Training Trajectories of Language Models Across Scales

Xi Victoria Lin, Danqi Chen, Luke Zettlemoyer, 2022, ArXiv.

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

Luke Zettlemoyer, Sewon Min, Jiaming Shen, 2022, ArXiv.

One Embedder, Any Task: Instruction-Finetuned Text Embeddings

Noah A. Smith, Luke Zettlemoyer, Mari Ostendorf, 2022, ArXiv.

Generalization through Memorization: Nearest Neighbor Language Models

Omer Levy, M. Lewis, Luke Zettlemoyer, 2019, International Conference on Learning Representations.

Scaling Laws for Generative Mixed-Modal Language Models

Karen Hambardzumyan, Naman Goyal, Omer Levy, 2023, ICML.

SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark

Sida I. Wang, Austin W. Hanjie, Luke Zettlemoyer, 2021, NeurIPS.

ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning

O. Yu. Golovneva, Luke Zettlemoyer, Spencer Poff, 2022, ICLR.

Mega: Moving Average Equipped Gated Attention

Luke Zettlemoyer, Graham Neubig, Xuezhe Ma, 2022, ICLR.

Lifted Probabilistic Inference with Counting Formulas

Leslie Pack Kaelbling, Kristian Kersting, Luke S. Zettlemoyer, 2008, AAAI.

Lexical Generalization in CCG Grammar Induction for Semantic Parsing

Mark Steedman, Luke S. Zettlemoyer, Sharon Goldwater, 2011, EMNLP.

SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark pdf

Luke Zettlemoyer, Victor Zhong, Karthik Narasimhan, 2021, ArXiv.

Deep Contextualized Word Representations

Luke S. Zettlemoyer, Kenton Lee, Mohit Iyyer, 2018, NAACL.

Language Grounding with 3D Objects

Mohit Shridhar, Yonatan Bisk, Jesse Thomason, 2021, CoRL.

Vision-and-Dialog Navigation

Jesse Thomason, Maya Cakmak, Luke Zettlemoyer, 2019, CoRL.

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension pdf

Omer Levy, Mike Lewis, Marjan Ghazvininejad, 2019, ACL.

Pre-training via Paraphrasing pdf

Armen Aghajanyan, Sida I. Wang, Marjan Ghazvininejad, 2020, NeurIPS.

REPLUG: Retrieval-Augmented Black-Box Language Models

M. Lewis, Luke Zettlemoyer, Minjoon Seo, 2023, ArXiv.

Questions Are All You Need to Train a Dense Passage Retriever

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, ArXiv.

Improving Passage Retrieval with Zero-Shot Question Generation

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, EMNLP.

Toolformer: Language Models Can Teach Themselves to Use Tools

Luke Zettlemoyer, M. Lomeli, Roberta Raileanu, 2023, NeurIPS.

AmbigQA: Answering Ambiguous Open-domain Questions

Julian Michael, Hannaneh Hajishirzi, Luke Zettlemoyer, 2020, EMNLP.

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Omer Levy, Danqi Chen, Daniel S. Weld, 2019, TACL.

Active Learning for Coreference Resolution using Discrete Annotation pdf

Gabriel Stanovsky, Luke Zettlemoyer, Belinda Li, 2020, ACL.

Controlled Crowdsourcing for High-Quality QA-SRL Annotation

Ido Dagan, Julian Michael, Jonathan Mamou, 2019, ACL.

VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding pdf

Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer, 2021, FINDINGS.

Multilingual Denoising Pre-training for Neural Machine Translation

Marjan Ghazvininejad, Luke Zettlemoyer, Sergey Edunov, 2020, Transactions of the Association for Computational Linguistics.

Better Fine-Tuning by Reducing Representational Collapse pdf

Armen Aghajanyan, Luke Zettlemoyer, Naman Goyal, 2020, ICLR.

Simple and Effective Retrieve-Edit-Rerank Text Generation

Nabil Hossain, Marjan Ghazvininejad, Luke Zettlemoyer, 2020, ACL.

Globally Coherent Text Generation with Neural Checklist Models

Yejin Choi, Luke S. Zettlemoyer, Chloé Kiddon, 2016, EMNLP.

A Theme-Rewriting Approach for Generating Algebra Word Problems

Luke S. Zettlemoyer, Ioannis Konstas, Hannaneh Hajishirzi, 2016, EMNLP.

Quantifying Adaptability in Pre-trained Language Models with 500 Tasks

Jane A. Yu, Luke Zettlemoyer, A. Halevy, 2021, NAACL.

DEMix Layers: Disentangling Domains for Modular Language Modeling pdf

Noah A. Smith, Ari Holtzman, Suchin Gururangan, 2021, NAACL.

BASE Layers: Simplifying Training of Large, Sparse Models

Naman Goyal, M. Lewis, Luke Zettlemoyer, 2021, ICML.

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning pdf

Armen Aghajanyan, Luke Zettlemoyer, Sonal Gupta, 2020, ACL.

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

Bryan C. Russell, Luke Zettlemoyer, S. Seitz, 2013 .

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

M. Lewis, Luke Zettlemoyer, Ari Holtzman, 2022, Conference on Empirical Methods in Natural Language Processing.

Prompting Contrastive Explanations for Commonsense Reasoning Tasks pdf

Julian Michael, Hannaneh Hajishirzi, Marjan Ghazvininejad, 2021, FINDINGS.

Detecting Hallucinated Content in Conditional Neural Sequence Generation pdf

Mona T. Diab, Chunting Zhou, Mona Diab, 2020, FINDINGS.

QuAC: Question Answering in Context

Eunsol Choi, Yejin Choi, Luke S. Zettlemoyer, 2018, EMNLP.

Learning to map sentences to logical form

Luke S. Zettlemoyer, Luke Zettlemoyer, 2012, UAI 2012.

FaVIQ: FAct Verification from Information-seeking Questions

Luke Zettlemoyer, Hannaneh Hajishirzi, Sewon Min, 2021, ACL.

DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions

Luke S. Zettlemoyer, Weijia Shi, Mandar Joshi, 2021, Annual Meeting of the Association for Computational Linguistics.

Aligned Cross Entropy for Non-Autoregressive Machine Translation

Omer Levy, Marjan Ghazvininejad, Vladimir Karpukhin, 2020, ICML.

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

Luke S. Zettlemoyer, Kenton Lee, Luheng He, 2018, NAACL.

Learning a Neural Semantic Parser from User Feedback pdf

Alvin Cheung, Luke S. Zettlemoyer, Ioannis Konstas, 2017, ACL.

Recurrent Additive Networks pdf

Omer Levy, Luke S. Zettlemoyer, Kenton Lee, 2017, ArXiv.

Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation

Luke Zettlemoyer, Hila Gonen, Marjan Ghazvininejad, 2023, ArXiv.

Mask-Predict: Parallel Decoding of Conditional Masked Language Models

Omer Levy, Marjan Ghazvininejad, Luke Zettlemoyer, 2019, EMNLP.

Semi-Autoregressive Training Improves Mask-Predict Decoding pdf

Omer Levy, Marjan Ghazvininejad, Luke Zettlemoyer, 2020, ArXiv.

Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog

Luke Zettlemoyer, Panupong Pasupat, Sonal Gupta, 2019, EMNLP.

Multi-hop Reading Comprehension through Question Decomposition and Rescoring pdf

Hannaneh Hajishirzi, Luke Zettlemoyer, Victor Zhong, 2019, ACL.

XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models

Naman Goyal, Luke Zettlemoyer, Hila Gonen, 2023, ArXiv.

CM3: A Causal Masked Multimodal Model of the Internet pdf

Dmytro Okhonko, Armen Aghajanyan, Mike Lewis, 2022, ArXiv.

Few-shot Learning with Multilingual Generative Language Models

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2022, EMNLP.

InCoder: A Generative Model for Code Infilling and Synthesis

Sida I. Wang, M. Lewis, Luke Zettlemoyer, 2022, ICLR.

Mapping Language to Code in Programmatic Context pdf

Alvin Cheung, Luke S. Zettlemoyer, Ioannis Konstas, 2018, EMNLP.

HTLM: Hyper-Text Pre-Training and Prompting of Language Models pdf

Dmytro Okhonko, Armen Aghajanyan, Mike Lewis, 2021, ICLR.

Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment

Sida I. Wang, Luke Zettlemoyer, Freda Shi, 2021, ACL.

Nearest Neighbor Machine Translation pdf

Mike Lewis, Dan Jurafsky, Urvashi Khandelwal, 2020, ICLR.

Sparse Networks from Scratch: Faster Training without Losing Performance pdf

Luke Zettlemoyer, Tim Dettmers, Luke Zettlemoyer, 2019, ArXiv.

Emerging Cross-lingual Structure in Pretrained Language Models

Luke Zettlemoyer, Veselin Stoyanov, Haoran Li, 2019, ACL.

DeLighT: Deep and Light-weight Transformer

Luke S. Zettlemoyer, Sachin Mehta, Hannaneh Hajishirzi, 2021, ICLR.

Large-Scale QA-SRL Parsing pdf

Luke S. Zettlemoyer, Julian Michael, Luheng He, 2018, ACL.

Crowdsourcing Question-Answer Meaning Representations

Ido Dagan, Luke S. Zettlemoyer, Julian Michael, 2017, NAACL.

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

Luke S. Zettlemoyer, Luheng He, Mike Lewis, 2015, EMNLP.

Paraphrase-Driven Learning for Open Question Answering

Oren Etzioni, Luke S. Zettlemoyer, Anthony Fader, 2013, ACL.

NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System pdf

Michael D. Ernst, Luke S. Zettlemoyer, Chenglong Wang, 2018, LREC.

Learning Planning Rules in Noisy Stochastic Worlds

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Hanna M. Pasula, 2005, AAAI.

Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases

Luke Zettlemoyer, Christopher Clark, Mark Yatskar, 2019, EMNLP.

Scaling Semantic Parsers with On-the-Fly Ontology Matching

Eunsol Choi, Luke S. Zettlemoyer, Yoav Artzi, 2013, EMNLP.

Transformers with convolutional context for ASR pdf

Luke S. Zettlemoyer, Abdel-rahman Mohamed, Dmytro Okhonko, 2019, ArXiv.

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

Yejin Choi, Luke S. Zettlemoyer, Ioannis Konstas, 2017, ACL.

Interactive Learning of Relation Extractors with Weak Supervision

Daniel S. Weld, Daniel S. Weld, Raphael Hoffmann, 2012 .

Déduction et application d'une grammaire non contextuelle à sujet ciblé

Luke Zettlemoyer, Chris Quirk, Pallavi Choudhury, 2014 .

Mise en Place: Unsupervised Interpretation of Instructional Recipes

Yejin Choi, Luke S. Zettlemoyer, Chloé Kiddon, 2015, EMNLP.

GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Luke Zettlemoyer, M. Lewis, Tim Dettmers, 2022, NeurIPS.

Muppet: Massive Multi-task Representations with Pre-Finetuning

Sonal Gupta, Armen Aghajanyan, Anchit Gupta, 2021, EMNLP.

JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation pdf

Luke Zettlemoyer, Srinivasan Iyer, Rajas Agashe, 2019, EMNLP.

Learning Programmatic Idioms for Scalable Semantic Parsing pdf

Alvin Cheung, Luke S. Zettlemoyer, Srinivasan Iyer, 2019, EMNLP.

Visual Generalization in Programming by Example

Henry Lieberman, Luke S. Zettlemoyer, Robert St. Amant, 2000, Your Wish is My Command.

The user interface as an agent environment

Luke S. Zettlemoyer, Robert St. Amant, Luke Zettlemoyer, 2000, AGENTS '00.

Supervised Open Information Extraction

Ido Dagan, Luke S. Zettlemoyer, Julian Michael, 2018, NAACL.

Noisy Channel Language Model Prompting for Few-Shot Text Classification

Luke Zettlemoyer, Hannaneh Hajishirzi, Sewon Min, 2021, ACL.

ART: Automatic multi-step reasoning and tool-use for large language models

Marco Tulio Ribeiro, Scott M. Lundberg, Luke Zettlemoyer, 2023, ArXiv.

REPLUG: Retrieval-Augmented Black-Box Language Models

M. Lewis, Luke Zettlemoyer, Minjoon Seo, 2023, ArXiv.

LIMA: Less Is More for Alignment

Omer Levy, M. Lewis, Luke Zettlemoyer, 2023, NeurIPS.

Stable and low-precision training for large-scale vision-language models

Ari S. Morcos, Luke Zettlemoyer, Ali Farhadi, 2023, ArXiv.

The case for 4-bit precision: k-bit Inference Scaling Laws

Luke Zettlemoyer, Tim Dettmers, 2022, ICML.

Compositional Questions Do Not Necessitate Multi-hop Reasoning pdf

Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, 2019, ACL.

Luna: Linear Unified Nested Attention pdf

Luke Zettlemoyer, Jonathan May, Xiang Kong, 2021, NeurIPS.

Natural Language to Code Translation with Execution

Sida I. Wang, Luke Zettlemoyer, Daniel Fried, 2022, EMNLP.

SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach pdf

Luke S. Zettlemoyer, Michael Petrochuk, Luke Zettlemoyer, 2018, EMNLP.

Grounded Adaptation for Zero-shot Executable Semantic Parsing

Sida I. Wang, M. Lewis, Luke Zettlemoyer, 2020, EMNLP.

A Discrete Hard EM Approach for Weakly Supervised Question Answering

Danqi Chen, Hannaneh Hajishirzi, Luke Zettlemoyer, 2019, EMNLP.

Iterative Search for Weakly Supervised Semantic Parsing

Luke S. Zettlemoyer, Eduard H. Hovy, Shikhar Murty, 2019, NAACL.

Multilingual Autoregressive Entity Linking pdf

Nicola De Cao, Fabio Petroni, Mikel Artetxe, 2021, TACL.

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

Danqi Chen, Luke Zettlemoyer, Hannaneh Hajishirzi, 2019, ArXiv.

Improving Passage Retrieval with Zero-Shot Question Generation

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, EMNLP.

Demystifying Prompts in Language Models via Perplexity Estimation

Noah A. Smith, Luke Zettlemoyer, Hila Gonen, 2022, EMNLP.

Questions Are All You Need to Train a Dense Passage Retriever

Devendra Singh Sachan, M. Lewis, Luke Zettlemoyer, 2022, TACL.

Broad-coverage CCG Semantic Parsing with AMR

Luke S. Zettlemoyer, Kenton Lee, Yoav Artzi, 2015, EMNLP.

Personalized Mathematical Word Problem Generation

Sumit Gulwani, Luke S. Zettlemoyer, Oleksandr Polozov, 2015, IJCAI.

QLoRA: Efficient Finetuning of Quantized LLMs

Luke Zettlemoyer, Luke Zettlemoyer, Ari Holtzman, 2023, NeurIPS.

Prompting Language Models for Linguistic Structure

Luke Zettlemoyer, Hila Gonen, Terra Blevins, 2022, ACL.

RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering

Luke Zettlemoyer, Victor Zhong, Weijia Shi, 2022, ArXiv.

Question Answering Infused Pre-training of General-Purpose Contextualized Representations

M. Lewis, Luke Zettlemoyer, Robin Jia, 2021, FINDINGS.

pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

Omer Levy, Eunsol Choi, Luke S. Zettlemoyer, 2018, NAACL.

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Pang Wei Koh, Hannaneh Hajishirzi, M. Lewis, 2023, ArXiv.

MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers

M. Lewis, Luke Zettlemoyer, L. Yu, 2023, NeurIPS.

Scaling Expert Language Models with Unsupervised Domain Discovery

Noah A. Smith, M. Lewis, Luke Zettlemoyer, 2023, ArXiv.

Neural Semantic Parsing

Luke S. Zettlemoyer, Alane Suhr, Pradeep Dasigi, 2018, ACL.

E3: Entailment-driven Extracting and Editing for Conversational Machine Reading

Luke Zettlemoyer, Victor Zhong, Luke Zettlemoyer, 2019, ACL.

Contrastive Decoding: Open-ended Text Generation as Optimization

Xiang Lisa Li, M. Lewis, Luke Zettlemoyer, 2022, ArXiv.

DeLighT: Very Deep and Light-weight Transformer pdf

Hannaneh Hajishirzi, Marjan Ghazvininejad, Luke Zettlemoyer, 2020, ArXiv.

Retrieval-Augmented Multimodal Language Modeling

M. Lewis, Luke Zettlemoyer, J. Leskovec, 2023, ICML.

Training Trajectories of Language Models Across Scales

Xi Victoria Lin, Danqi Chen, Luke Zettlemoyer, 2022, ACL.

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

Luke Zettlemoyer, Sewon Min, Jiaming Shen, 2022, ACL.

AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization

Luke Zettlemoyer, Hannaneh Hajishirzi, Pradeep Dasigi, 2022, ICLR.

Modeling Missing Data in Distant Supervision for Information Extraction

Oren Etzioni, Luke S. Zettlemoyer, Alan Ritter, 2013, TACL.

BERT for Coreference Resolution: Baselines and Analysis

Daniel S. Weld, Mandar Joshi, Omer Levy, 2019, EMNLP.

Ultra-Fine Entity Typing pdf

Omer Levy, Eunsol Choi, Yejin Choi, 2018, ACL.

A visual medium for programmatic control of interactive applications

Luke S. Zettlemoyer, Robert St. Amant, Luke Zettlemoyer, 1999, CHI '99.

IBOTS: agent control through the user interface

Luke S. Zettlemoyer, Robert St. Amant, Martin S. Dulberg, 1998, IUI '99.

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

Eunsol Choi, Yejin Choi, Luke S. Zettlemoyer, 2016, ACL.

Extreme Extraction: Only One Hour per Relation pdf

Luke S. Zettlemoyer, Daniel S. Weld, Raphael Hoffmann, 2015, ArXiv.

Mixture of Prompt Experts for Generalizable and Interpretable Question Answering

Jordan L. Boyd-Graber, Luke Zettlemoyer, Luke Zettlemoyer, 2023, ArXiv.

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

Luke S. Zettlemoyer, Lucy Vanderwende, Mark Yatskar, 2014, *SEMEVAL.

Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles

Luke Zettlemoyer, Christopher Clark, Mark Yatskar, 2020, FINDINGS.

Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models

Luke Zettlemoyer, Terra Blevins, Haoqiang Kang, 2023, ArXiv.

REPLUG: Retrieval-Augmented Black-Box Language Models

M. Lewis, Luke Zettlemoyer, Minjoon Seo, 2023, NAACL.

Nonparametric Masked Language Modeling

M. Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi, 2022, ACL.

Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling pdf

Omer Levy, Luke S. Zettlemoyer, Kenton Lee, 2018, ACL.

Joint A* CCG Parsing and Semantic Role Labelling

Luke S. Zettlemoyer, Luheng He, Mike Lewis, 2015, EMNLP.

Programming by example: visual generalization in programming by example

Henry Lieberman, Luke S. Zettlemoyer, Robert St. Amant, 2000, CACM.

An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction

Mandar Joshi, Luke Zettlemoyer, John Thickstun, 2020, EMNLP.

Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing

Luke S. Zettlemoyer, Asish Ghoshal, Yashar Mehdad, 2021, ICLR.

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

Luke S. Zettlemoyer, Dieter Fox, Cynthia Matuszek, 2014, AAAI.

Learning Probabilistic Relational Planning Rules

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Hanna M. Pasula, 2004, ICAPS.

Logical Particle Filtering

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Hanna M. Pasula, 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

One Embedder, Any Task: Instruction-Finetuned Text Embeddings

Noah A. Smith, Noah A. Smith, Luke Zettlemoyer, 2022, ACL.

Morpho-syntactic Lexical Generalization for CCG Semantic Parsing

Luke S. Zettlemoyer, Tom Kwiatkowski, Adrienne Wang, 2014, EMNLP.

Representation Deficiency in Masked Language Modeling

Luke Zettlemoyer, Marjan Ghazvininejad, Yuning Mao, 2023, ArXiv.

Contrastive Decoding: Open-ended Text Generation as Optimization

Xiang Lisa Li, M. Lewis, Luke Zettlemoyer, 2022, ACL.

Inducing Semantic Roles Without Syntax

Luke S. Zettlemoyer, Julian Michael, Luke Zettlemoyer, 2021, FINDINGS.

Improving Semantic Parsing for Task Oriented Dialog pdf

Luke S. Zettlemoyer, Arash Einolghozati, Sonal Gupta, 2019, ArXiv.

Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?

Luke Zettlemoyer, Ari Holtzman, Peter West, 2023, ArXiv.

Learning Distributions over Logical Forms for Referring Expression Generation

Luke S. Zettlemoyer, Yoav Artzi, Nicholas FitzGerald, 2013, EMNLP.

Efficient Large Scale Language Modeling with Mixtures of Experts

Xi Victoria Lin, Punit Singh Koura, Myle Ott, 2022, EMNLP.

Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

Luke Zettlemoyer, Yulia Tsvetkov, M. Lewis, 2023, NAACL.

Language Contamination Explains the Cross-lingual Capabilities of English Pretrained Models

Luke Zettlemoyer, Terra Blevins, 2022, ArXiv.

RevMiner: an extractive interface for navigating reviews on a smartphone

Oren Etzioni, Luke S. Zettlemoyer, Kevin Clark, 2012, UIST.

BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation

Luke Zettlemoyer, Sida Wang, Marjan Ghazvininejad, 2021, NAACL-HLT.

QANom: Question-Answer driven SRL for Nominalizations

Ido Dagan, Dan Roth, Jonathan Mamou, 2020, COLING.

Animated pedagogical agents in knowledge-based learning environments

James C. Lester, Charles B. Callaway, Luke Zettlemoyer, 2001 .

Revisiting Machine Translation for Cross-lingual Classification

Luke Zettlemoyer, Angela Fan, Mikel Artetxe, 2023, ArXiv.

Stop: A Dataset for Spoken Task Oriented Semantic Parsing

Luke Zettlemoyer, Abdel-rahman Mohamed, A. Elkahky, 2022, 2022 IEEE Spoken Language Technology Workshop (SLT).

Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing

Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, 2020, EMNLP.

Few-shot Learning with Multilingual Language Models pdf

Zornitsa Kozareva, Vishrav Chaudhary, Myle Ott, 2021, ArXiv.

The Referential Reader: A Recurrent Entity Network for Anaphora Resolution pdf

Luke S. Zettlemoyer, Fei Liu, Jacob Eisenstein, 2019, ACL.

SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore

Noah A. Smith, Noah A. Smith, Luke Zettlemoyer, 2023, ArXiv.

Learning to map sentences to logical form

Luke Zettlemoyer, 2012, UAI 2012.

3D Wikipedia

Bryan C. Russell, Luke Zettlemoyer, S. Seitz, 2013, ACM Trans. Graph..

Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

O. Yu. Golovneva, Luke Zettlemoyer, Yaniv Taigman, 2023, ArXiv.

Neural Metaphor Detection in Context

Eunsol Choi, Yejin Choi, Luke S. Zettlemoyer, 2018, EMNLP.

M2D2: A Massively Multi-Domain Language Modeling Dataset

Luke Zettlemoyer, Victor Zhong, Suchin Gururangan, 2022, EMNLP.

Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI

Luke Zettlemoyer, Amjad Almahairi, Madian Khabsa, 2022, AAAI.

Combining world and interaction models for human-robot collaborations

Dieter Fox, Andrzej Pronobis, Cynthia Matuszek, 2013, AAAI 2013.

Shepherd: A Critic for Language Model Generation

O. Yu. Golovneva, Luke Zettlemoyer, Ramakanth Pasunuru, 2023, ArXiv.

Learning to Relate Literal and Sentimental Descriptions of Visual Properties

Luke S. Zettlemoyer, Svitlana Volkova, Asli Çelikyilmaz, 2013, NAACL.

Lightly Supervised Learning of Procedural Dialog Systems

Luke S. Zettlemoyer, Svitlana Volkova, Chris Quirk, 2013, ACL.

Interactive Learning and its Role in Pervasive Robotics

Luke Zettlemoyer, D. Fox, E. Herbst, 2012 .

ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning

O. Yu. Golovneva, Luke Zettlemoyer, Spencer Poff, 2022, ArXiv.

Habitable 3D Learning Environments for Situated Learning

Luke S. Zettlemoyer, James C. Lester, William H. Bares, 1998, Intelligent Tutoring Systems.

Learning and Planning with Probabilistic Relational Rules

Luke Zettlemoyer, Hanna Pasula, Leslie Pack, 2004 .

Grounded Adaptation for Zero-shot Executable Semantic Parsing

Sida I. Wang, Luke Zettlemoyer, Victor Zhong, 2020, EMNLP.

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Pang Wei Koh, M. Lewis, Luke Zettlemoyer, 2023, EMNLP.

The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants

Satya Narayan Shukla, Naman Goyal, Luke Zettlemoyer, 2023, ArXiv.

Contrastive Decoding: Open-ended Text Generation as Optimization

Xiang Lisa Li, M. Lewis, Luke Zettlemoyer, 2022, ACL.

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

Luke Zettlemoyer, Iz Beltagy, Hannaneh Hajishirzi, 2022, ACL.

Selective Annotation Makes Language Models Better Few-Shot Learners

Noah A. Smith, Chen Henry Wu, Luke Zettlemoyer, 2023, ICLR.

Stable and low-precision training for large-scale vision-language models

Ari S. Morcos, Luke Zettlemoyer, Ali Farhadi, 2023, NeurIPS.

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

Mark Steedman, Luke S. Zettlemoyer, Sharon Goldwater, 2012, EACL.

Global Neural CCG Parsing with Optimality Guarantees

Luke S. Zettlemoyer, Kenton Lee, Mike Lewis, 2016, EMNLP.

The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages

Eric Michael Smith, Luke Zettlemoyer, M. Costa-jussà, 2023, WMT.

XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models

Naman Goyal, Luke Zettlemoyer, Hila Gonen, 2023, EMNLP.

Recognizing and Imitating Programmer Style: Adversaries in Program Authorship Attribution

Luke S. Zettlemoyer, Tadayoshi Kohno, Lucy Simko, 2018, Proc. Priv. Enhancing Technol..

Multi-Label Learning with Posterior Regularization

Xi Victoria Lin, Luke Zettlemoyer, B. Taskar, 2014 .

Large-Scale QA-SRL Parsing

Luke Zettlemoyer, Nicholas FitzGerald, Julian Michael, 2018, ACL.

Prompting Language Models for Linguistic Structure

Luke Zettlemoyer, Hila Gonen, Terra Blevins, 2022, ACL.

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

M. Lewis, Luke Zettlemoyer, Ari Holtzman, 2022, EMNLP.

Human-in-the-Loop Parsing

Luke S. Zettlemoyer, Julian Michael, Luheng He, 2016, EMNLP.

Getting MoRE out of Mixture of Language Model Reasoning Experts

Jordan L. Boyd-Graber, Luke Zettlemoyer, Chenglei Si, 2023, EMNLP.

Emerging Cross-lingual Structure in Pretrained Language Models

Luke Zettlemoyer, Veselin Stoyanov, Haoran Li, 2019, ACL.

Learning STRIPS Operators from Noisy and Incomplete Observations

Ronald P. A. Petrick, Luke Zettlemoyer, Mark Steedman, 2012, UAI.

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Dragomir R. Radev, Noah A. Smith, Sida I. Wang, 2022, EMNLP.