Luke Zettlemoyer

Also published as: Luke S. Zettlemoyer


2019

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Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog
Panupong Pasupat | Sonal Gupta | Karishma Mandyam | Rushin Shah | Mike Lewis | Luke Zettlemoyer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally. In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy. Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed.

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A Discrete Hard EM Approach for Weakly Supervised Question Answering
Sewon Min | Danqi Chen | Hannaneh Hajishirzi | Luke Zettlemoyer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2–10%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis.

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Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Christopher Clark | Mark Yatskar | Luke Zettlemoyer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment models often learn that particular key words imply entailment, irrespective of context, and visual question answering models learn to predict prototypical answers, without considering evidence in the image. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Experiments on five datasets with out-of-domain test sets show significantly improved robustness in all settings, including a 12 point gain on a changing priors visual question answering dataset and a 9 point gain on an adversarial question answering test set.

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Cloze-driven Pretraining of Self-attention Networks
Alexei Baevski | Sergey Edunov | Yinhan Liu | Luke Zettlemoyer | Michael Auli
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with BERT. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.

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Learning Programmatic Idioms for Scalable Semantic Parsing
Srinivasan Iyer | Alvin Cheung | Luke Zettlemoyer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, state of the art (SOTA) semantic parsers still map natural language instructions to source code by building the code syntax tree one node at a time. In this paper, we introduce an iterative method to extract code idioms from large source code corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax trees, and train semantic parsers to apply these idioms during decoding. Applying idiom-based decoding on a recent context-dependent semantic parsing task improves the SOTA by 2.2% BLEU score while reducing training time by more than 50%. This improved speed enables us to scale up the model by training on an extended training set that is 5× larger, to further move up the SOTA by an additional 2.3% BLEU and 0.9% exact match. Finally, idioms also significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL dataset, when training data is limited.

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JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation
Rajas Agashe | Srinivasan Iyer | Luke Zettlemoyer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Interactive programming with interleaved code snippet cells and natural language markdown is recently gaining popularity in the form of Jupyter notebooks, which accelerate prototyping and collaboration. To study code generation conditioned on a long context history, we present JuICe, a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments. Compared with existing contextual code generation datasets, JuICe provides refined human-curated data, open-domain code, and an order of magnitude more training data. Using JuICe, we train models for two tasks: (1) generation of the API call sequence in a code cell, and (2) full code cell generation, both conditioned on the NL-Code history up to a particular code cell. Experiments using current baseline code generation models show that both context and distant supervision aid in generation, and that the dataset is challenging for current systems.

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BERT for Coreference Resolution: Baselines and Analysis
Mandar Joshi | Omer Levy | Luke Zettlemoyer | Daniel Weld
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication.

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Mask-Predict: Parallel Decoding of Conditional Masked Language Models
Marjan Ghazvininejad | Omer Levy | Yinhan Liu | Luke Zettlemoyer
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster.

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Better Character Language Modeling through Morphology
Terra Blevins | Luke Zettlemoyer
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint. Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows. We then transfer morphological supervision across languages to improve performance in the low-resource setting.

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Evaluating Gender Bias in Machine Translation
Gabriel Stanovsky | Noah A. Smith | Luke Zettlemoyer
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., “The doctor asked the nurse to help her in the operation”). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word “doctor”). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available at https://github.com/gabrielStanovsky/mt_gender.

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E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
Victor Zhong | Luke Zettlemoyer
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3.

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Compositional Questions Do Not Necessitate Multi-hop Reasoning
Sewon Min | Eric Wallace | Sameer Singh | Matt Gardner | Hannaneh Hajishirzi | Luke Zettlemoyer
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions can be answered with a single hop if they target specific entity types, or the facts needed to answer them are redundant. Our analysis is centered on HotpotQA, where we show that single-hop reasoning can solve much more of the dataset than previously thought. We introduce a single-hop BERT-based RC model that achieves 67 F1—comparable to state-of-the-art multi-hop models. We also design an evaluation setting where humans are not shown all of the necessary paragraphs for the intended multi-hop reasoning but can still answer over 80% of questions. Together with detailed error analysis, these results suggest there should be an increasing focus on the role of evidence in multi-hop reasoning and possibly even a shift towards information retrieval style evaluations with large and diverse evidence collections.

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The Referential Reader: A Recurrent Entity Network for Anaphora Resolution
Fei Liu | Luke Zettlemoyer | Jacob Eisenstein
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operation causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates strong performance with purely incremental text processing.

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Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Sewon Min | Victor Zhong | Luke Zettlemoyer | Hannaneh Hajishirzi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast subquestion generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.

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Iterative Search for Weakly Supervised Semantic Parsing
Pradeep Dasigi | Matt Gardner | Shikhar Murty | Luke Zettlemoyer | Eduard Hovy
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates between searching for consistent logical forms and maximizing the marginal likelihood of the retrieved ones. This training scheme lets us iteratively train models that provide guidance to subsequent ones to search for logical forms of increasing complexity, thus dealing with the problem of spuriousness. We evaluate these techniques on two hard datasets: WikiTableQuestions (WTQ) and Cornell Natural Language Visual Reasoning (NLVR), and show that our training algorithm outperforms the previous best systems, on WTQ in a comparable setting, and on NLVR with significantly less supervision.

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pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
Mandar Joshi | Eunsol Choi | Omer Levy | Daniel Weld | Luke Zettlemoyer
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function of each word’s representation, which is learned by maximizing the pointwise mutual information (PMI) with the contexts in which the the two words co-occur. We add these representations to the cross-sentence attention layer of existing inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing existing word embeddings. Experiments show a gain of 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI. Our representations also aid in better generalization with gains of around 6-7% on adversarial SQuAD datasets, and 8.8% on the adversarial entailment test set by Glockner et al. (2018).

2018

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NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
Xi Victoria Lin | Chenglong Wang | Luke Zettlemoyer | Michael D. Ernst
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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AllenNLP: A Deep Semantic Natural Language Processing Platform
Matt Gardner | Joel Grus | Mark Neumann | Oyvind Tafjord | Pradeep Dasigi | Nelson F. Liu | Matthew Peters | Michael Schmitz | Luke Zettlemoyer
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.

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SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach
Michael Petrochuk | Luke Zettlemoyer
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The SimpleQuestions dataset is one of the most commonly used benchmarks for studying single-relation factoid questions. In this paper, we present new evidence that this benchmark can be nearly solved by standard methods. First, we show that ambiguity in the data bounds performance at 83.4%; many questions have more than one equally plausible interpretation. Second, we introduce a baseline that sets a new state-of-the-art performance level at 78.1% accuracy, despite using standard methods. Finally, we report an empirical analysis showing that the upperbound is loose; roughly a quarter of the remaining errors are also not resolvable from the linguistic signal. Together, these results suggest that the SimpleQuestions dataset is nearly solved.

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Neural Metaphor Detection in Context
Ge Gao | Eunsol Choi | Yejin Choi | Luke Zettlemoyer
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.

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Dissecting Contextual Word Embeddings: Architecture and Representation
Matthew Peters | Mark Neumann | Luke Zettlemoyer | Wen-tau Yih
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated.

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Mapping Language to Code in Programmatic Context
Srinivasan Iyer | Ioannis Konstas | Alvin Cheung | Luke Zettlemoyer
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Source code is rarely written in isolation. It depends significantly on the programmatic context, such as the class that the code would reside in. To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to “return the smallest element” in a particular member variable list). We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. We also present a detailed error analysis suggesting that there is significant room for future work on this task.

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QuAC: Question Answering in Context
Eunsol Choi | He He | Mohit Iyyer | Mark Yatskar | Wen-tau Yih | Yejin Choi | Percy Liang | Luke Zettlemoyer
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.

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Syntactic Scaffolds for Semantic Structures
Swabha Swayamdipta | Sam Thomson | Kenton Lee | Luke Zettlemoyer | Chris Dyer | Noah A. Smith
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on PropBank semantics, frame semantics, and coreference resolution, achieving competitive performance on all three tasks.

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Supervised Open Information Extraction
Gabriel Stanovsky | Julian Michael | Luke Zettlemoyer | Ido Dagan
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model outperforms the existing state-of-the-art Open IE systems on benchmark datasets.

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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Mohit Iyyer | John Wieting | Kevin Gimpel | Luke Zettlemoyer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) “fool” pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.

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Deep Contextualized Word Representations
Matthew Peters | Mark Neumann | Mohit Iyyer | Matt Gardner | Christopher Clark | Kenton Lee | Luke Zettlemoyer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

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Crowdsourcing Question-Answer Meaning Representations
Julian Michael | Gabriel Stanovsky | Luheng He | Ido Dagan | Luke Zettlemoyer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available QAMR data and annotation scheme should support significant future work.

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Higher-Order Coreference Resolution with Coarse-to-Fine Inference
Kenton Lee | Luheng He | Luke Zettlemoyer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We introduce a fully-differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations. This enables the model to softly consider multiple hops in the predicted clusters. To alleviate the computational cost of this iterative process, we introduce a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor, enabling more aggressive pruning without hurting accuracy. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient.

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Ultra-Fine Entity Typing
Eunsol Choi | Omer Levy | Yejin Choi | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict ultra-fine types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets.

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Large-Scale QA-SRL Parsing
Nicholas FitzGerald | Julian Michael | Luheng He | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.

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Deep RNNs Encode Soft Hierarchical Syntax
Terra Blevins | Omer Levy | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it. These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling. In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges. This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision.

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Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Luheng He | Kenton Lee | Omer Levy | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.

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Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum
Omer Levy | Kenton Lee | Nicholas FitzGerald | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. We present an alternative view to explain the success of LSTMs: the gates themselves are versatile recurrent models that provide more representational power than previously appreciated. We do this by decoupling the LSTM’s gates from the embedded simple RNN, producing a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. Ablations on a range of problems demonstrate that the gating mechanism alone performs as well as an LSTM in most settings, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

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Neural Semantic Parsing
Matt Gardner | Pradeep Dasigi | Srinivasan Iyer | Alane Suhr | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

Semantic parsing, the study of translating natural language utterances into machine-executable programs, is a well-established research area and has applications in question answering, instruction following, voice assistants, and code generation. In the last two years, the models used for semantic parsing have changed dramatically with the introduction of neural encoder-decoder methods that allow us to rethink many of the previous assumptions underlying semantic parsing. We aim to inform those already interested in semantic parsing research of these new developments in the field, as well as introduce the topic as an exciting research area to those who are unfamiliar with it. Current approaches for neural semantic parsing share several similarities with neural machine translation, but the key difference between the two fields is that semantic parsing translates natural language into a formal language, while machine translation translates it into a different natural language. The formal language used in semantic parsing allows for constrained decoding, where the model is constrained to only produce outputs that are valid formal statements. We will describe the various approaches researchers have taken to do this. We will also discuss the choice of formal languages used by semantic parsers, and describe why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. We will then describe a particularly challenging setting for semantic parsing, where there is additional context or interaction that the parser must take into account when translating natural language to formal language, and give an overview of recent work in this direction. Finally, we will introduce some tools available in AllenNLP for doing semantic parsing research.

2017

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End-to-end Neural Coreference Resolution
Kenton Lee | Luheng He | Mike Lewis | Luke Zettlemoyer
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.

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Zero-Shot Relation Extraction via Reading Comprehension
Omer Levy | Minjoon Seo | Eunsol Choi | Luke Zettlemoyer
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.

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Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Ioannis Konstas | Srinivasan Iyer | Mark Yatskar | Yejin Choi | Luke Zettlemoyer
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.

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Deep Semantic Role Labeling: What Works and What’s Next
Luheng He | Kenton Lee | Mike Lewis | Luke Zettlemoyer
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.

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Learning a Neural Semantic Parser from User Feedback
Srinivasan Iyer | Ioannis Konstas | Alvin Cheung | Jayant Krishnamurthy | Luke Zettlemoyer
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its full expressivity, bypassing any intermediate meaning representations. These models are immediately deployed online to solicit feedback from real users to flag incorrect queries. Finally, the popularity of SQL facilitates gathering annotations for incorrect predictions using the crowd, which is directly used to improve our models. This complete feedback loop, without intermediate representations or database specific engineering, opens up new ways of building high quality semantic parsers. Experiments suggest that this approach can be deployed quickly for any new target domain, as we show by learning a semantic parser for an online academic database from scratch.

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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi | Eunsol Choi | Daniel Weld | Luke Zettlemoyer
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.

2016

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Globally Coherent Text Generation with Neural Checklist Models
Chloé Kiddon | Luke Zettlemoyer | Yejin Choi
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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A Theme-Rewriting Approach for Generating Algebra Word Problems
Rik Koncel-Kedziorski | Ioannis Konstas | Luke Zettlemoyer | Hannaneh Hajishirzi
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Human-in-the-Loop Parsing
Luheng He | Julian Michael | Mike Lewis | Luke Zettlemoyer
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Global Neural CCG Parsing with Optimality Guarantees
Kenton Lee | Mike Lewis | Luke Zettlemoyer
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Document-level Sentiment Inference with Social, Faction, and Discourse Context
Eunsol Choi | Hannah Rashkin | Luke Zettlemoyer | Yejin Choi
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Summarizing Source Code using a Neural Attention Model
Srinivasan Iyer | Ioannis Konstas | Alvin Cheung | Luke Zettlemoyer
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods
Annie Louis | Michael Roth | Bonnie Webber | Michael White | Luke Zettlemoyer
Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods

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LSTM CCG Parsing
Mike Lewis | Kenton Lee | Luke Zettlemoyer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language
Luheng He | Mike Lewis | Luke Zettlemoyer
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Mise en Place: Unsupervised Interpretation of Instructional Recipes
Chloé Kiddon | Ganesa Thandavam Ponnuraj | Luke Zettlemoyer | Yejin Choi
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Joint A* CCG Parsing and Semantic Role Labelling
Mike Lewis | Luheng He | Luke Zettlemoyer
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Event Detection and Factuality Assessment with Non-Expert Supervision
Kenton Lee | Yoav Artzi | Yejin Choi | Luke Zettlemoyer
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Broad-coverage CCG Semantic Parsing with AMR
Yoav Artzi | Kenton Lee | Luke Zettlemoyer
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Scalable Semantic Parsing with Partial Ontologies
Eunsol Choi | Tom Kwiatkowski | Luke Zettlemoyer
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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Morpho-syntactic Lexical Generalization for CCG Semantic Parsing
Adrienne Wang | Tom Kwiatkowski | Luke Zettlemoyer
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Semantic Parsing with Combinatory Categorial Grammars
Yoav Artzi | Nicholas Fitzgerald | Luke Zettlemoyer
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Semantic parsers map natural language sentences to formal representations of their underlying meaning. Building accurate semantic parsers without prohibitive engineering costs is a long-standing, open research problem.The tutorial will describe general principles for building semantic parsers. The presentation will be divided into two main parts: learning and modeling. In the learning part, we will describe a unified approach for learning Combinatory Categorial Grammar (CCG) semantic parsers, that induces both a CCG lexicon and the parameters of a parsing model. The approach learns from data with labeled meaning representations, as well as from more easily gathered weak supervision. It also enables grounded learning where the semantic parser is used in an interactive environment, for example to read and execute instructions. The modeling section will include best practices for grammar design and choice of semantic representation. We will motivate our use of lambda calculus as a language for building and representing meaning with examples from several domains.The ideas we will discuss are widely applicable. The semantic modeling approach, while implemented in lambda calculus, could be applied to many other formal languages. Similarly, the algorithms for inducing CCG focus on tasks that are formalism independent, learning the meaning of words and estimating parsing parameters. No prior knowledge of CCG is required. The tutorial will be backed by implementation and experiments in the University of Washington Semantic Parsing Framework (UW SPF, http://yoavartzi.com/spf).

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Learning to Automatically Solve Algebra Word Problems
Nate Kushman | Yoav Artzi | Luke Zettlemoyer | Regina Barzilay
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Context-dependent Semantic Parsing for Time Expressions
Kenton Lee | Yoav Artzi | Jesse Dodge | Luke Zettlemoyer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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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)

2013

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Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
Yoav Artzi | Luke Zettlemoyer
Transactions of the Association for Computational Linguistics, Volume 1

The context in which language is used provides a strong signal for learning to recover its meaning. In this paper, we show it can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision. The joint nature provides crucial benefits by allowing situated cues, such as the set of visible objects, to directly influence learning. It also enables algorithms that learn while executing instructions, for example by trying to replicate human actions. Experiments on a benchmark navigational dataset demonstrate strong performance under differing forms of supervision, including correctly executing 60% more instruction sets relative to the previous state of the art.

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Modeling Missing Data in Distant Supervision for Information Extraction
Alan Ritter | Luke Zettlemoyer | Mausam | Oren Etzioni
Transactions of the Association for Computational Linguistics, Volume 1

Distant supervision algorithms learn information extraction models given only large readily available databases and text collections. Most previous work has used heuristics for generating labeled data, for example assuming that facts not contained in the database are not mentioned in the text, and facts in the database must be mentioned at least once. In this paper, we propose a new latent-variable approach that models missing data. This provides a natural way to incorporate side information, for instance modeling the intuition that text will often mention rare entities which are likely to be missing in the database. Despite the added complexity introduced by reasoning about missing data, we demonstrate that a carefully designed local search approach to inference is very accurate and scales to large datasets. Experiments demonstrate improved performance for binary and unary relation extraction when compared to learning with heuristic labels, including on average a 27% increase in area under the precision recall curve in the binary case.

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Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves
Hannaneh Hajishirzi | Leila Zilles | Daniel S. Weld | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Automatic Idiom Identification in Wiktionary
Grace Muzny | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Scaling Semantic Parsers with On-the-Fly Ontology Matching
Tom Kwiatkowski | Eunsol Choi | Yoav Artzi | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Learning Distributions over Logical Forms for Referring Expression Generation
Nicholas FitzGerald | Yoav Artzi | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Learning to Relate Literal and Sentimental Descriptions of Visual Properties
Mark Yatskar | Svitlana Volkova | Asli Celikyilmaz | Bill Dolan | Luke Zettlemoyer
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Paraphrase-Driven Learning for Open Question Answering
Anthony Fader | Luke Zettlemoyer | Oren Etzioni
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Lightly Supervised Learning of Procedural Dialog Systems
Svitlana Volkova | Pallavi Choudhury | Chris Quirk | Bill Dolan | Luke Zettlemoyer
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Semantic Parsing with Combinatory Categorial Grammars
Yoav Artzi | Nicholas FitzGerald | Luke Zettlemoyer
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Tutorials)

2012

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Discriminative Learning for Joint Template Filling
Einat Minkov | Luke Zettlemoyer
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings
Tom Kwiatkowski | Sharon Goldwater | Luke Zettlemoyer | Mark Steedman
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann | Congle Zhang | Xiao Ling | Luke Zettlemoyer | Daniel S. Weld
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Bootstrapping Semantic Parsers from Conversations
Yoav Artzi | Luke Zettlemoyer
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Lexical Generalization in CCG Grammar Induction for Semantic Parsing
Tom Kwiatkowski | Luke Zettlemoyer | Sharon Goldwater | Mark Steedman
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification
Tom Kwiatkowksi | Luke Zettlemoyer | Sharon Goldwater | Mark Steedman
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Reading between the Lines: Learning to Map High-Level Instructions to Commands
S.R.K. Branavan | Luke Zettlemoyer | Regina Barzilay
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Reinforcement Learning for Mapping Instructions to Actions
S.R.K. Branavan | Harr Chen | Luke Zettlemoyer | Regina Barzilay
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Learning Context-Dependent Mappings from Sentences to Logical Form
Luke Zettlemoyer | Michael Collins
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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A Generative Model for Parsing Natural Language to Meaning Representations
Wei Lu | Hwee Tou Ng | Wee Sun Lee | Luke S. Zettlemoyer
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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Online Learning of Relaxed CCG Grammars for Parsing to Logical Form
Luke Zettlemoyer | Michael Collins
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Selective Phrase Pair Extraction for Improved Statistical Machine Translation
Luke Zettlemoyer | Robert Moore
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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