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End-to-end Neural Coreference Resolution
TLDR
This work introduces the first end-to-end coreference resolution model, trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Expand
Deep Semantic Role Labeling: What Works and What's Next
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 useExpand
Higher-Order Coreference Resolution with Coarse-to-Fine Inference
TLDR
This work introduces a fully-differentiable approximation to higher-order inference for coreference resolution that significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient. Expand
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
TLDR
The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links and supports construction of a scientific knowledge graph, which is used to analyze information in scientific literature. Expand
PAWS: Paraphrase Adversaries from Word Scrambling
TLDR
PAWS (Paraphrase Adversaries from Word Scrambling), a new dataset with 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap, is introduced, providing an effective instrument for driving further progress on models that better exploit structure, context, and pairwise comparisons. Expand
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
TLDR
This work proposes an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them, and makes independent decisions about what relationship, if any, holds between every possible word-span pair. Expand
Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language
TLDR
The results show that non-expert annotators can produce high quality QA-SRL data, and also establish baseline performance levels for future work on this task, and introduce simple classifierbased models for predicting which questions to ask and what their answers should be. Expand
A general framework for information extraction using dynamic span graphs
TLDR
This framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains and is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset. Expand
Large-Scale QA-SRL Parsing
TLDR
A new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA- SRL parser are presented, and neural models for two QA -SRL subtasks are presented: detecting argument spans for a predicate and generating questions to label the semantic relationship. Expand
Crowdsourcing Question-Answer Meaning Representations
TLDR
A crowdsourcing scheme is developed to show that QAMRs can be labeled with very little training, and a qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets. Expand
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