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Controlled Crowdsourcing for High-Quality QA-SRL Annotation
An improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase is presented, which yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. Expand
QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
A novel representation of discourse relations as QA pairs is proposed, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Expand
QANom: Question-Answer driven SRL for Nominalizations
We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relationsExpand
Crowdsourcing a High-Quality Gold Standard for QA-SRL
An improved QA-SRL annotation protocol is presented, involving crowd-worker selection and training, followed by data consolidation, yielding more consistent annotations and greater coverage, which will facilitate future replicable research of natural semantic annotations. Expand
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
This work proposes to align predicate-argument relations across texts, providing a potential scaffold for information consolidation, and goes beyond clustering coreferring mentions and model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. Expand