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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of theseExpand
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Supervised Open Information Extraction
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 taggingExpand
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Creating a Large Benchmark for Open Information Extraction
Open information extraction (Open IE) was presented as an unrestricted variant of traditional information extraction. It has been gaining substantial attention, manifested by a large number ofExpand
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Getting More Out Of Syntax with PropS
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, manyExpand
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Crowdsourcing Question-Answer Meaning Representations
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing schemeExpand
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Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models
Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are contextdependent and include long, varied and unconventional descriptions as compared to moreExpand
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Evaluating Gender Bias in Machine Translation
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 ofExpand
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Open IE as an Intermediate Structure for Semantic Tasks
Semantic applications typically extract information from intermediate structures derived from sentences, such as dependency parse or semantic role labeling. In this paper, we study Open InformationExpand
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Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequentlyExpand
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Semantics as a Foreign Language
We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreignExpand
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